Methods and apparatus for audio equalization

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed for audio equalization. Example apparatus disclosed herein include a volume adjuster to apply equalization adjustments to an audio signal to generate an equalized audio signal, the equalization adjustments output from a neural network in response to an input feature set; a thresholding controller to: detect an irregularity in a frequency representation of the audio signal after application of the equalization adjustments, the irregularity corresponding to a change in volume between adjacent frequency values exceeding a threshold; and adjust a volume at a first frequency value of the adjacent frequency values to reduce the irregularity; an equalization (EQ) curve generator to generate an EQ curve to apply to the audio signal when the irregularity has been reduced; and a frequency to time domain converter to output the equalized audio signal in a time domain based on the EQ curve.

RELATED APPLICATION

This patent arises from an application claiming benefit of U.S.Provisional Patent Application Ser. No. 62/750,113, which was filed onOct. 24, 2018; U.S. Provisional Patent Application Ser. No. 62/816,813,which was filed on Mar. 11, 2019; U.S. Provisional Patent ApplicationSer. No. 62/816,823, which was filed on Mar. 11, 2019; and U.S.Provisional Patent Application Ser. No. 62/850,528, which was filed onMay 20, 2019. U.S. Provisional Patent Application Ser. No. 62/750,113;U.S. Provisional Patent Application Ser. No. 62/816,813; U.S.Provisional Patent Application Ser. No. 62/816,823; and U.S. ProvisionalPatent Application Ser. No. 62/850,528 are hereby incorporated herein byreference in their entirety. Priority to U.S. Provisional PatentApplication Ser. No. 62/750,113; U.S. Provisional Patent ApplicationSer. No. 62/816,813; U.S. Provisional Patent Application Ser. No.62/816,823; and U.S. Provisional Patent Application Ser. No. 62/850,528is hereby claimed.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audio playback settings, and, moreparticularly, to methods and apparatus for audio equalization.

BACKGROUND

In recent years, a multitude of media of varying characteristics hasbeen delivered using an increasing number of channels. Media can bereceived using more traditional channels (e.g., the radio, mobilephones, etc.), or using more recently developed channels, such as usingInternet-connected streaming devices. As these channels have developed,systems which are able to process and output audio from multiple sourceshave been developed as well. These audio signals may have differingcharacteristics (e.g., dynamic range, volume, etc.). Some automobilemedia systems, for example, are capable of delivering media from compactdiscs (CD's), Bluetooth connecting devices, universal serial bus (USB)connected devices, Wi-Fi connected devices, auxiliary inputs, and othersources.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example environmentconstructed in accordance with the teachings of this disclosure fordynamic playback setting adjustment based on real-time analysis of mediacharacteristics.

FIG. 2 is a block diagram showing additional detail of the media unit ofFIG. 1 to perform techniques for audio equalization in accordance withat least a first implementation, a second implementation, and a thirdimplementation of the teachings of this disclosure.

FIG. 3 is a block diagram showing additional detail of the contentprofile engine of FIG. 1 according to the second implementation.

FIG. 4 is a block diagram showing additional detail of the audioequalization (EQ) engine of FIG. 1.

FIG. 5 is a flowchart representative of example machine readableinstructions that may be executed to implement the media unit of FIGS. 1and 2 to dynamically adjust media playback settings based on real-timeanalysis of media characteristics according to the first implementation.

FIG. 6 is a flowchart representative of example machine readableinstructions that may be executed to implement the media unit 106 ofFIGS. 1 and 2 to personalize equalization settings.

FIG. 7 is a flowchart representative of example machine readableinstructions that may be executed to implement the audio EQ engine totrain the EQ neural network according to the first implementation.

FIG. 8A is a first spectrogram of an audio signal which has undergonedynamic audio playback setting adjustment based on real-time analysis ofaudio characteristics, but without a smoothing filter, according to thefirst implementation.

FIG. 8B is a plot depicting average gain values for frequency values forthe first spectrogram of FIG. 8A.

FIG. 9A is a second spectrogram of an audio signal which has undergonedynamic audio playback setting adjustment based on real-time analysis ofaudio characteristics including a smoothing filter, according to thefirst implementation.

FIG. 9B is a plot depicting average gain values for frequency values inthe second spectrogram of FIG. 9A.

FIG. 10 is a flowchart representative of example machine readableinstructions that may be executed to implement the content profileengine of FIGS. 1 and 3 to deliver profile information along with astream of content to a playback device, according to the secondimplementation.

FIG. 11 is a flowchart representative of example machine readableinstructions that may be executed to implement the media unit of FIGS. 1and 2 to play content using modified playback settings, according to thesecond implementation.

FIG. 12 is a flowchart representative of example machine readableinstructions that may be executed to implement the media unit of FIGS. 1and 2 to adjust playback settings based on profile informationassociated with content, according to the second implementation.

FIGS. 13A-13B are block diagrams of example content profiles, inaccordance with the teachings of this disclosure.

FIG. 14 is a flowchart representative of machine readable instructionsthat may be executed to implement the media unit of FIGS. 1 and 2 toperform real-time audio equalization according to the thirdimplementation.

FIG. 15 is a flowchart representative of machine readable instructionsthat may be executed to implement the media unit of FIGS. 1 and 2 tosmooth an equalization curve according to the third implementation.

FIG. 16 is a flowchart representative of machine readable instructionsthat may be executed to implement the audio EQ engine of FIGS. 1 and 4to assemble a dataset to train and/or validate a neural network based onreference audio signals according to the third implementation.

FIG. 17A is an example representation of an equalized audio signal priorto performing the smoothing techniques illustrated and described inconnection with FIG. 15.

FIG. 17B is an example representation of the audio signal in FIG. 17Aafter performing the smoothing techniques illustrated and described inconnection with FIG. 15, according to the third implementation.

FIG. 18 is a block diagram of an example first processing platformstructured to execute the instructions of FIGS. 5, 6, 11, 12, 14, and 15to implement the media unit of FIGS. 1 and 2.

FIG. 19 is a block diagram of an example second processing platformstructure to execute the instructions of FIGS. 7 and 16 to implement theaudio EQ engine of FIGS. 1 and 4.

FIG. 20 is a block diagram of an example second processing platformstructure to execute the instructions of FIG. 10 to implement thecontent profile engine of FIGS. 1 and 3.

In general, the same reference numbers will be used throughout thedrawing(s) and accompanying written description to refer to the same orlike parts.

DETAILED DESCRIPTION

In conventional media processing implementations, audio signalsassociated with different media may have different characteristics. Forexample, different audio tracks may have different frequency profiles(e.g., varying volume levels at different frequencies of the audiosignal), different overall (e.g., average) volumes, pitch, timbre, etc.For example, media on one CD may be recorded and/or mastered differentlythan media of another CD. Similarly, media retrieved from a streamingdevice may have significantly different audio characteristics than mediaretrieved from an uncompressed medium such as a CD and may also differfrom media retrieved from the same device via a different applicationand/or audio compression level. As users increasingly listen to media ofa variety of different sources and of a variety of genres and types,differences in audio characteristics between sources and between mediaof the same source can become very noticeable, and potentiallyirritating to a listener. Audio equalization is a technique utilized toadjust volume levels of different frequencies in an audio signal. Forexample, equalization can be performed to increase the presence of lowfrequency signals, mid-frequency signals, and/or high-frequency signalsbased on a preference associated with a genre of music, an era of music,a user preference, a space in which the audio signal is output, etc.However, the optimal or preferred equalization settings may varydepending on the media being presented. Hence, a listener may need tofrequently adjust equalization settings to optimize the listeningexperience based on a change in media (e.g., a change in the genre, achange in era, a change in overall volume of the track, etc.).

In some conventional approaches, an equalization setting can be selectedthat is associated with a specific genre or type of music. For example,in a media unit on a vehicle, a listener may be able to select anequalizer for “Rock,” which is configured to boost frequencies that auser may want to hear more of, and cut other frequencies which may beoverpowering, based on typical characteristics of Rock music. However,such genre-specific broadly applied, equalization settings fail toaddress significant differences between different songs, and furtherstill require a user to manually change the equalization setting whenthey begin a new track of a different genre, which occurs frequently onradio stations and audio streaming applications.

In a first implementation, example methods, apparatus, systems andarticles of manufacture disclosed herein, dynamically adjust audioplayback settings (e.g., equalization settings, volume settings, etc.)based on real-time characteristics of audio signals. Examples disclosedherein determine a frequency representation (e.g., a CQT representation)of a sample (e.g., a three second sample) of the audio signal and querya neural network to determine equalization settings specific to theaudio signal. In some examples disclosed herein, the equalizationsettings include a plurality of filters (e.g., low-shelf filters,peaking filters, high shelf filters, etc.), one or more of which can beselected and applied to the audio signal. In example methods, apparatus,systems and articles of manufacture disclosed herein, the neural networkthat outputs equalization settings is trained using a library ofreference media corresponding to a plurality of equalization profilesthat are optimized for the media (e.g., as determined by audioengineers).

In the first implementation, example methods, apparatus, systems, andarticles of manufacture disclosed herein, query audio samples (e.g.,including three seconds of audio) against the neural network on aregular basis (e.g., every second) to determine equalization settingsfor the profile, to account for changes in the audio signal over time(e.g., different portions of the track having different characteristics,transitions in songs, transitions in genres, etc.). Example methods,apparatus, systems, and articles of manufacture disclosed herein,utilize a smoothing filter (e.g., an exponential smoothing algorithm, aone-pole recursive smoothing filter, etc.) to transition between filtersettings to avoid perceptible changes in the equalization settings.

Additionally, example methods, systems, and articles of manufacturingfor modifying the playback of content using pre-processed profileinformation are described in accordance with a second implementation.Example methods, systems, and articles of manufacture access a stream ofcontent to be delivered to a playback device, identify a piece ofcontent within the stream of content to be delivered to the playbackdevice, determine a profile for the identified piece of content, anddeliver the determined profile to the playback device. These operationsmay be performed automatically (e.g., in real-time) on-the-fly.

In the second implementation, example methods, systems, and articles ofmanufacture receive a stream of content at a playback device, accessprofile information associated with the stream of content, and modifyplayback of the stream of content based on the accessed profileinformation. For example, example methods, systems, and articles ofmanufacture receive and/or access an audio stream along with profileinformation identifying a mood or other characteristics assigned to theaudio stream and modify playback settings of the playback device (e.g.,equalization settings) based on the profile information.

In the second implementation, example methods, systems and articles ofmanufacture may, therefore, pre-process a content stream provided by acontent provider to determine a profile for the content stream, anddeliver the profile to a playback device, which may play the contentstream with an adjusted, modified, and/or optimized playback experience,among other things.

In a third implementation, example methods, apparatus, systems, andarticles of manufacture disclosed herein, analyze and equalize incomingaudio signals (e.g., from a storage device, from a radio, from astreaming service, etc.) without a need for user input or adjustments.The techniques disclosed herein analyze incoming audio signals todetermine average volume values during a buffer period for a pluralityof frequency ranges, standard deviation values during the buffer periodfor the plurality of frequency ranges, and an energy of the incomingaudio signal. By utilizing average frequency values over the bufferperiod, sudden short-term changes in the incoming audio signal aresmoothed out when determining an equalization curve to apply, therebyavoiding drastic changes in the equalization settings.

In the third implementation, example methods, apparatus, systems, andarticles of manufacture disclosed herein generate an input feature setincluding the average volume values during the buffer period for theplurality of frequency ranges and/or the standard deviation valuesduring the buffer period for the plurality of frequency ranges and inputthe input feature set into a neural network. The example methods,apparatus, systems, and articles of manufacture disclosed herein utilizea neural network trained on a plurality of reference audio signals and aplurality of equalization curves generated by audio engineers. In someexamples, the reference audio signals and corresponding equalizationcurves are tagged (e.g., associated) with an indication of the specificaudio engineer that generated the equalization curve, to enable theneural network to learn the different equalization styles andpreferences of the different audio engineers. Example methods,apparatus, systems, and articles of manufacture disclosed herein receivegains/cuts (e.g., volume adjustments) corresponding to specificfrequency ranges from the neural network. In some examples, thegains/cuts are applied to a frequency representation of the incomingaudio signal and then the equalized frequency representation is analyzedto determine whether there are any abnormalities (e.g., sharp spikes ordips in the volume level across frequencies).

In accordance with the third implementation, example methods, apparatus,systems, and articles of manufacture disclosed herein employ athresholding technique to remove abnormalities in the equalized audiosignal prior to finalizing the equalization curve (e.g., the gains/cutsfor a plurality of frequency ranges) that is to be applied to the audiosignal. In some examples, the thresholding technique analyzes sets ofadjacent frequency values (e.g., three or more adjacent frequencyvalues) and determines whether the difference in volume between theseadjacent frequency values (e.g., as determined by calculating the secondderivative over the frequency range) exceeds a threshold when the EQgains/cuts 241 from the neural network are applied. In some examples, inresponse to determining the difference in volume between adjacentfrequency values exceeds the threshold, a volume corresponding to acentral one of the frequency values can be adjusted to the midpointbetween the volume levels at the adjacent frequency values, therebyeliminating the spike or dip in the frequency representation of theequalized audio signal. This adjustment has the subjective effect of amore pleasant EQ curve when compared to an EQ curve that has dips andpeaks (e.g., localized outliers) across the spectral envelope.

In the third implementation, example methods, apparatus, systems, andarticles of manufacture disclosed herein measure an energy value (e.g.,an RMS value) for the incoming audio signal and an energy value afterthe equalization curve is applied to a representation of the incomingaudio signal to attempt to normalize overall volume before and afterequalization. For example, if the equalization curve being applied tothe audio signal boosts volume in more frequency ranges than it cutsvolume, the overall energy of the equalized audio signal may be higher.In some such examples, volume normalization can be performed on theequalized audio signal to remove any noticeable volume changes betweenthe incoming audio signal and the equalized audio signal.

In the third implementation, example methods, apparatus, systems, andarticles of manufacture disclosed herein improve audio equalizationtechniques by dynamically adjusting equalization settings to account forchanges in a source (e.g., radio, media stored on a mobile device,compact disc, etc.) providing the incoming audio signal orcharacteristics (e.g., genre, era, mood, etc.) of media represented inthe incoming audio signal. Example techniques disclosed herein utilize aneural network intelligently trained on audio signals equalized byexpert audio engineers, enabling the neural network to learn preferencesand skills from various audio engineers. Example techniques disclosedherein further improve the equalization adjustments provided by theneural network by performing thresholding techniques to ensure the finalequalization curve is smooth and does not have major volume disparitiesbetween adjacent frequency ranges.

FIG. 1 is a block diagram illustrating an example environment 100constructed in accordance with the teachings of this disclosure fordynamic playback settings adjustment based on real-time analysis ofmedia characteristics. The example environment 100 includes mediadevices 102, 104 that transmit audio signals to a media unit 106. Themedia unit 106 processes the audio signals (e.g., performing audioequalization techniques as disclosed herein) and transmits the signalsto an audio amplifier 108, which subsequently outputs the amplifiedaudio signal to be presented via an output device 110.

In the example of FIG. 1, the media devices 102, 104 and/or the mediaunit 106 communicate, via a network 112, such as the Internet, with anexample content provider 114 or content source (e.g., a broadcaster, anetwork, a website, and so on), that provides various types ofmultimedia content, such as audio content and/or video content. Examplecontent providers 114 may include terrestrial or satellite radiostations, online music services, online video services, televisionbroadcasters and/or distributors, networked computing devices (e.g.,mobile devices on a network), local audio or music applications, and soon. It should be noted that the content (e.g., audio and/or videocontent) may be obtained from any source. For example, the term “contentsource” is intended include users and other content owners (such asartists, labels, movie studios, etc.). In some examples, the contentsource is a publicly accessible website such as YouTube™.

In some examples, the network 112 may be any network or communicationmedium that enables communication between the content provider 114, themedia device 102, the media device 104, the media unit 106, and/or othernetworked devices. The example network 112 may be or include a wirednetwork, a wireless network (e.g., a mobile network), a radio ortelecommunications network, a satellite network, and so on. For example,the network 112 may include one or more portions that constitute aprivate network (e.g., a cable television network or a satellite radionetwork), a public network (e.g., over-the-air broadcast channels or theInternet), and so on.

The example media device 102 of the illustrated example of FIG. 1 is aportable media player (e.g., an MP3 player). The example media device102 stores or receives audio signals and/or video signals correspondingto media from the content provider 114. For example, the media device102 can receive audio signals and/or video signals from the contentprovider 114 over the network 112. The example media device 102 iscapable of transmitting the audio signals to other devices. In theillustrated example of FIG. 1, the media device 102 transmits audiosignals to the media unit 106 via an auxiliary cable. In some examples,the media device 102 may transmit audio signals to the media unit 106via any other interface. In some examples, the media device 102 and themedia unit 106 may be the same device (e.g., the media unit 106 may be amobile device, which is capable of performing audio equalizationtechniques disclosed herein on audio being presented on the mobiledevice).

The example media device 104 of the illustrated example of FIG. 1 is amobile device (e.g., a cell phone). The example media device 104 storesor receives audio signals corresponding to media and is capable oftransmitting the audio signals to other devices. In the illustratedexample of FIG. 1, the media device 104 transmits audio signals to themedia unit 106 wirelessly. In some examples, the media device 104 mayuse Wi-Fi, Bluetooth®, and/or any other technology to transmit audiosignals to the media unit 106. In some examples, the media device 104may interact with components of a vehicle or other devices for alistener to select media for presentation in the vehicle. The mediadevices 102, 104 may be any devices which are capable of storing and/oraccessing audio signals. In some examples, the media devices 102, 104may be integral to the vehicle (e.g., a CD player, a radio, etc.).

The example media unit 106 of the illustrated example of FIG. 1 iscapable of receiving audio signals and processing them. In theillustrated example of FIG. 1, the example media unit 106 receives mediasignals from the media devices 102, 104 and processes them to performaudio equalization techniques as disclosed herein. The example mediaunit 106 is capable of monitoring audio that is being output by theoutput device 110 to determine the average volume level of audiosegments, audio characteristics (e.g., frequency, amplitude, timevalues, etc.) in real time. In some examples, the example media unit 106is implemented as software and is included as part of another device,available either through a direct connection (e.g., a wired connection)or through a network (e.g., available on the cloud). In some examples,the example media unit 106 may be incorporated with the audio amplifier108 and the output device 110 and may output audio signals itselffollowing processing of the audio signals.

In some examples, the media device 102, the media device 104, and/or themedia unit 106 can communicate with the content provider 114, and/or acontent profile engine 116 via the network 112. In additional oralternative examples, the media device 102 and/or the media device 104can include a tuner configured to receive a stream of audio or videocontent and play the stream of audio or video content by processing thestream and outputting information (e.g., digital or analog) usable by adisplay of the media device 102 and/or the media device 104 to presentor play back the audio or video content to a user associated with themedia device 102 and/or the media device 104. The media device 102and/or the media device 104 may also include a display or other userinterface configured to display the processed stream of content and/orassociated metadata. The display may be a flat-panel screen, a plasmascreen, a light emitting diode (LED) screen, a cathode ray tube (CRT), aliquid crystal display (LCD), a projector, and so on.

In some examples, the content provider 114, the content profile engine116, the media device 102, the media device 104, and/or the media unit106 may include one or more fingerprint generators 115 configured togenerate identifiers for content being transmitted or broadcast by thecontent provider 114 and/or received or accessed by the media device102, the media device 104, and/or the media unit 106. For example, thefingerprint generators 115 may include a reference fingerprint generator(e.g., a component that calculates a hash value from a portion ofcontent) that is configured to generate reference fingerprints or otheridentifiers of received content, among other things.

In some examples, the media unit 106 can be configured to modify theplayback experience of content played by the media device 102 and/or themedia device 104. For example, the media unit 106 can access a profileassociated with a stream of content and utilize the profile to modify,adjust, and/or otherwise control various playback settings (e.g.,equalization settings) associated with a quality or character for theplayback of the content. In an example where the content is video orother visual content, the playback settings may include color palettesettings, color layout settings, brightness settings, font settings,artwork settings, and so on.

The example audio amplifier 108 of the illustrated example of FIG. 1 isa device that is capable of receiving the audio signal that has beenprocessed (e.g., equalized) by the media unit 106 and performing theappropriate playback setting adjustments (e.g., amplification ofspecific bands of the audio signal, volume adjustments based on a userinput, etc.) for output to the output device 110. In some examples, theaudio amplifier 108 may be incorporated into the output device 110. Insome examples, the audio amplifier 108 amplifies the audio signal basedon an amplification output value from the media unit 106. In someexamples, the audio amplifier 108 amplifies the audio signal based on aninput from a listener (e.g., a passenger or driver in a vehicleadjusting a volume selector). In additional or alternative examples, theaudio is output directly from the media unit 106 instead of beingcommunicated to an amplifier.

The example output device 110 of the illustrated example of FIG. 1 is aspeaker. In some examples, the output device 110 may be multiplespeakers, headphones, or any other device capable of presenting audiosignals to a listener. In some examples, the output device 110 may becapable of outputting visual elements as well (e.g., a television withspeakers). In some examples, the output device 110 may be integrated inthe media unit 106. For example, if the media unit 106 is a mobiledevice, the output device 110 may be a speaker integrated in orotherwise connected with (e.g., via Bluetooth®, auxiliary cable, etc.)the mobile device. In some such examples, the output device 110 may beheadphones connected to the mobile device.

In some examples, the content profile engine 116 can access, via thenetwork 112, a stream of content provided by the content provider 114,and perform various processes to determine, generate, and/or select aprofile or profile information for the stream of content. For example,the content profile engine 116 can identify the stream of content (e.g.,using audio or video fingerprint comparisons), and determine a profilefor the identified stream of content. The content profile engine 116 maydeliver the profile to the media device 102, the media device 104,and/or the media unit 106, which receives the profile along with thestream of content, and plays the stream of content using certainplayback settings that are associated and/or selected based oninformation within the received profile, among other things.

In the example of FIG. 1, the environment includes an audio EQ engine118 capable of providing a trained model for use by the media unit 106.In some examples, the trained model resides on the audio EQ engine 118,while in some examples the trained model is exported for direct use onthe media unit 106. Machine learning techniques, whether deep learningnetworks or other experiential/observational learning system, can beused to optimize results, locate an object in an image, understandspeech and convert speech into text, and improve the relevance of searchengine results, for example.

While the illustrated example environment 100 of FIG. 1 is described inreference to a playback setting adjustment (e.g., audio equalization)implementation in a vehicle, some or all of the devices included in theexample environment 100 may be implemented in any environment, and inany combination. For example, the media unit 106, along with any of theaudio amplifier 108 and/or the output device 110 may be implemented(e.g., entirely or partially) in a mobile phone, which can performplayback setting adjustment (e.g., audio equalization) utilizingtechniques disclosed herein on any media being presented from the mobiledevice (e.g., streaming music, media stored locally on the mobiledevice, radio, etc.). In some examples, the environment 100 may be in anentertainment room of a house, wherein the media devices 102, 104 may bepersonal stereo systems, one or more televisions, laptops, otherpersonal computers, tablets, other mobile devices (e.g., smart phones),gaming consoles, virtual reality devices, set top boxes, or any otherdevices capable of accessing and/or transmitting media. Additionally, insome examples, the media may include visual elements as well (e.g.,television shows, films, etc.).

In some examples, the content profile engine 116 may be part of thecontent provider 114, the media device 102, the media device 104, and/orthe media unit 106. As another example, the media device 102 and/or themedia device 104 can include the content provider 114 (e.g., the mediadevice 102 and/or the media device 104 is a mobile device having a musicplayback application and the content provider 114 is a local store ofsongs and other audio), among other configurations.

FIG. 2 is a block diagram showing additional detail of the media unit106 of FIG. 1 to perform techniques for audio equalization in accordancewith at least the first implementation, the second implementation, andthe third implementation of the teachings of this disclosure. Theexample media unit 106 receives an input media signal 202 and processesthe signal to determine audio and/or video characteristics. The audioand/or video characteristics are then utilized to determine appropriateaudio and/or video playback adjustments based on the characteristics ofthe input media signal 202. When the input media signal 202 is an audiosignal, the media unit 106 transmits an output audio signal to the audioamplifier 108 for amplification prior to output by the output device110.

The example media unit 106 includes an example signal transformer 204,an example equalization (EQ) model query generator 206, an example EQfilter settings analyzer 208, an example EQ personalization manager 210,an example device parameter analyzer 212, an example historical EQmanager 214, an example user input analyzer 216, an example EQ filterselector 218, an example EQ adjustment implementor 220, an examplesmoothing filter configurator 222, an example data store 224, and anexample update monitor 226. The example media unit 106 further includesan example fingerprint generator 227 and an example synchronizer 228.The example media unit 106 additionally includes an example buffermanager 230, an example time to frequency domain converter 232, anexample volume calculator 234, an example energy calculator 236, anexample input feature set generator 238, an example EQ curve manager240, an example volume adjuster 242, and example thresholding controller244, an example EQ curve generator 246, an example volume normalizer248, and an example frequency to time domain converter 250.

The example media unit 106 is configured to operate according to atleast three implementations. In a first implementation, the media unit106 equalizes media in real-time according to filter settings receivedfrom a neural network in response to a query including a frequencyrepresentation of the input media signal 202. In the firstimplementation, after processing the filter settings, the media unit 106can generate an output media signal 252 that is equalized according toat least some of the filter settings. In some examples of the firstimplementation, the media unit 106 can additionally apply one or moresmoothing filters to the equalized version of the input media signal 202prior to outputting the output media signal 252.

In a second implementation, the media unit 106 equalizes mediadynamically according to one or more profiles received from a contentprofile engine (e.g., the content profile engine 116). In the secondimplementation, after processing the one or more profiles, the mediaunit 106 can generate the output media signal 252 that is equalizedaccording to at least some of the one or more profiles. In some examplesof the second implementation, the media unit 106 can additionally applypersonalized equalization to the input media signal 202 prior tooutputting the output media signal 252.

In a third implementation, the media unit 106 equalizes media inreal-time according to equalization gain and cut values received from aneural network in response to an input feature set including featuresbased on the input media signal 202. In the third implementation, afterprocessing the filter settings, the media unit 106 can generate theoutput media signal 252 that is equalized according to at least some ofthe gain and cut values. In some examples of the third implementation,the media unit 106 can apply thresholding to the equalized version ofthe input media signal 202 to remove local outliers in the output mediasignal 252.

First Implementation: Filter-Based Equalization

In the first implementation, the example input media signal 202 can bean audio signal that is to be processed and output for presentation. Theinput media signal 202 may be accessed from a radio signal (e.g., an FMsignal, an AM signal, a satellite radio signal, etc.), from a compactdisc, from an auxiliary cable (e.g., connected to a media device), froma Bluetooth signal, from a Wi-Fi signal, or from any other medium. Theinput media signal 202 is accessed by the signal transformer 204, the EQadjustment implementor 220, and/or the update monitor 226. The inputmedia signal 202 is transformed by the EQ adjustment implementor 220 tobe output by the media unit 106 as the output media signal 252.

The example signal transformer 204 of the illustrated example of FIG. 2transforms the input media signal 202 to a frequency and/orcharacteristic representation of the audio signal. For example, thesignal transformer 204 can transform the input media signal 202 to a CQTrepresentation. In some examples, the signal transformer 204 transformsthe input media signal 202 using a Fourier transform. In some examples,the signal transformer 204 continually transforms the input media signal202 into a frequency and/or characteristic representation, while inother examples the signal transformer 204 transforms the input mediasignal 202 at a regular interval or in response to a demand (e.g.,whenever it is required for dynamic audio playback settings adjustment)from one or more other components of the media unit 106. In someexamples, the signal transformer 204 transforms the input media signal202 in response to a signal from the update monitor 226 (e.g.,indicating it is time to update the audio playback settings). The signaltransformer 204 of the illustrated example communicates the frequencyand/or characteristic representation of the input media signal 202 tothe EQ model query generator 206, the fingerprint generator 227, and/orthe synchronizer 228.

The EQ model query generator 206 of the illustrated example of FIG. 2generates and communicates EQ queries 207 based on the frequency and/orcharacteristic representation of the input media signal 202. The EQmodel query generator 206 selects one or more frequencyrepresentation(s) corresponding to a sample time frame (e.g., a threesecond sample) of the input media signal 202 and communicates thefrequency representation(s) to a neural network (e.g., the EQ neuralnetwork 402 of FIG. 4). The sample time frame corresponds to a durationof the input media signal 202 that should be considered when determiningthe audio playback settings. In some examples, an operator (e.g., alistener, an audio engineer, etc.) can configure the sample time frame.In some examples, the EQ model query generator 206 communicates thequery 207 (including the frequency representation(s) of the input mediasignal 202) to a neural network via a network. In some examples, the EQmodel query generator 206 queries a model that is stored on (e.g., atthe data store 224), and executes on, the media unit 106. In someexamples the EQ model query generator 206 generates a new query 207 todetermine updated audio playback settings in response to a signal fromthe update monitor 226.

The EQ filter settings analyzer 208 of the illustrated example of FIG. 2accesses EQ filter settings 209 and calculates filter coefficients to beapplied to the input media signal 202. The EQ filter settings analyzer208 accesses EQ filter settings 209 output by the EQ neural network(e.g., the EQ neural network 402 of FIG. 4), which may include one ormore gain values, frequency values, and/or quality factor (Q) values. Insome examples, the EQ filter settings 209 include multiple filters(e.g., one low shelf filter, four peaking filters, one high shelffilter, etc.). In some such examples, individual filters includemultiple adjustment parameters, such as one or more gain values, one ormore frequency values, and/or one or more Q values. For example, for anaudio signal to which multiple filters are to be applied, the multiplefilters can include respective adjustment parameters includingrespective gain values, respective frequency values, and respective Qvalues (e.g., respective quality factor values). In some examples, theEQ filter settings analyzer 208 utilizes different equations tocalculate filter coefficients based on the filter types. For example, afirst equation may be utilized to determine a first filter coefficientfor a low shelf filter, and a second equation may be utilized todetermine a second filter coefficient for a high shelf filter. The EQfilter settings analyzer 208 communicates with the EQ filter selector218 to determine which of the one or more sets of EQ filter settings 209received by the EQ filter settings analyzer 208 should be processed(e.g., by calculating filter coefficients) to be applied to the inputmedia signal 202.

The example EQ personalization manager 210 of the illustrated example ofFIG. 2 generates personalized equalization settings (e.g., apersonalized EQ setting, personalized EQ settings, curves, filtersettings, etc.) which can be combined with dynamically generated filtersettings from the neural network to account for personal preferences ofa listener. The EQ personalization manager 210 includes an exampledevice parameter analyzer 212, an example historical EQ manager 214, andan example user input analyzer 216.

The device parameter analyzer 212 analyzes parameters associated withthe media unit 106 and/or a source device providing the input mediasignal 202. For example, the device parameter analyzer 212 can indicatean app from which the input media signal 202 originated. In some suchexamples, different apps may be associated with different equalizationprofiles. For example, an audio signal from an app associated withaudiobooks may have a different optimal equalization curve relative toan audio signal from an audio signal from an app associated withfitness.

In some examples, the device parameter analyzer 212 determines alocation of the device. For example, the device parameter analyzer 212can determine a location of the media unit 106 and/or the location of adevice providing the input media signal 202 to the media unit 106. Forexample, if the media unit 106 is integrated in a mobile device, and thelocation of the mobile device is a gym, a different personalizedequalization curve may be generated than if the mobile device is locatedat a user's home or workplace. In some examples, the device parameteranalyzer 212 determines whether the location of the mobile device iswithin a geofence of an area for which a personalized equalizationsetting (e.g., a personalized EQ setting) is determined (e.g., the gym,home, workplace, a library, etc.).

In some examples, the device parameter analyzer 212 determines a user ofthe media unit 106 and/or a user of a device supplying the input mediasignal 202 to the media unit. For example, if the media unit 106 isintegrated into a mobile device, the device parameter analyzer 212 maydetermine a user of the mobile device based on a login associated withthe user device and/or another identifier associated with the userdevice. In some examples, a user may be asked to select a user profileto indicate who is utilizing the mobile device and/or other deviceassociated with the media unit 106.

The device parameter analyzer 212 of the illustrated example outputsand/or adjusts a personalized EQ curve based on any parameters which thedevice parameter analyzer 212 is able to access (e.g., a location, auser identifier, a source identifier, etc.).

The historical EQ manager 214 of the illustrated example of FIG. 2maintains historical data pertaining to past equalization curvesutilized to enable subsequent personalized EQ curve adjustments. Forexample, if a user frequently listens to rock music, and frequentlyutilizes EQ curves which are most suitable for rock music, thehistorical EQ manager 214 can help adjust and/or generate a personalizedEQ curve based on the user's typical music preferences. For example, thehistorical EQ manager 214 can generate a personalized EQ curve based ona defined historical listening period. For example, the historical EQmanager 214 can generate a personalized EQ curve based on 1 hour ofprevious listening, based the past 24 hours of listening, and/or for anyother time period. Stated differently, the historical EQ manager 214 cangenerate and/or adjust a personalized EQ curve based on EQ settingsassociated with a previous period of time. The historical EQ manager 214takes the EQ curves that are being generated in real time by the EQfilter settings analyzer 208 and/or the neural network and adds thosesettings for each band (e.g., each of the five bands) of EQ into a longterm, personalized EQ filter that averages the settings for thehistorical period. The average curve that the system has seen for thehistorical period becomes the personalization EQ curve. This curve willreflect the average EQ of the type of music that the user has beenlistening to. For instance, if a user has been listening to Heavy Metalfor the past 60 minutes, that user will have a different EQ curve storedin his/her user profile than if that user had been listening to Top 40Pop for the past 60 minutes.

The averaging operation could be a rolling average, an IIR filter, (allpole filter) with the coefficients set to average over the time period,or any other averaging technique. This averaging can alleviate the needto hold a long duration of buffer information. By utilizing historicalEQ data, the EQ settings can be made to have a degree of “stickiness,”whereby the system gradually learns a listener's preference over timeand provides more useful equalization curves.

In some examples, the historical EQ manager 214 determines a smallsubset of genres that could be used with a table look up for a given EQcurve for each genre (Rock, Country, Spoken, Hip Hop, etc.). Based onthis subset of genres, a user can EQ curves can be generated, adjusted,or selected.

The user input analyzer 216 of the illustrated example of FIG. 2accesses and responds to user inputs corresponding to equalizationsettings. For example, a user may provide inputs as to whether aspecific equalization setting is preferred (e.g., by pressing a “like”button, by providing a user rating, etc.). These inputs can then beutilized when generating the personalized EQ curve to more heavilyweight those equalization settings which a user indicated they prefer.In some examples, user preferences are stored for a defined period(e.g., a few months, a year, etc.). In some examples, user preferencesare stored in associated with particular user accounts (e.g., userlogins identified by the device parameter analyzer 212). In someexamples, the user input analyzer 216 receives “reset” signals from alistener, which indicate that the user would like to undo any automatedpersonalized equalization which is being applied to the audio signal. Insome examples, the user input analyzer 216 adjusts a strength of theequalization based on a strength input from the listener.

The example EQ filter selector 218 of the illustrated example of FIG. 2selects one or more of the filters (e.g., one or more of a low shelffilter, a peaking filter, a high shelf filter, etc.) represented by theEQ filter settings received by the EQ filter settings analyzer 208 to beapplied to the input media signal 202. The EQ filter selector 218 of theillustrated example selects one or more filters that have the highestmagnitude gain (and thus will likely have the largest impact on theinput media signal 202). In some examples, such as when a specificnumber of filters are to be utilized (e.g., five band filters), one ormore additional filters represented by the EQ filter settings may bediscarded. In some examples, the EQ filter selector 218 determines thefilters which will have the least perceptible impact to the listener anddiscards these filters. For example, the EQ filter selector mayintegrate over one or more filter's spectral envelope and compare thisoutput between filters to determine which of the filters represented bythe EQ filter settings should be discarded. In some examples, the EQfilter selector 218 communicates to the EQ filter settings analyzer 208and/or the EQ adjustment implementor 220 which of the filters are to beapplied to the input media signal 202.

The EQ adjustment implementor 220 of the illustrated example of FIG. 2applies the filters selected by the EQ filter selector 218 and analyzedby the EQ filter settings analyzer 208. For example, the EQ adjustmentimplementor 220 can adjust amplitude, frequency, and/or phasecharacteristics of the input media signal 202 based on the filtercoefficients calculated by the EQ filter settings analyzer 208. In someexamples, the EQ adjustment implementor 220 smoothly transitions fromprevious audio playback settings to updated audio playback settings(e.g., new filter configurations) using a smoothing filter as indicatedby the smoothing filter configurator 222. The EQ adjustment implementor220 outputs the output media signal 252 after applying one or moreequalization filter(s).

In some examples, the EQ adjustment implementor 220 blends between anequalization profile generated based on EQ filter settings 209 from theneural network and a personalized EQ from the EQ personalization manager210. For example, a user profile EQ curve can be blended with the realtime curve that is generated by the neural network. In some examples, aweight is used to blend the EQ curves; multiple weights may be used aswell. As an example, a final EQ curve that the shapes the audio that theuser ends up listening to may be 0.5 times the current EQ based ondynamically generated filter settings and is 0.5 times the personalizedEQ curve. As another example, the first number could be 0.25 to thecurrent EQ based on dynamically generated filter settings and 0.75 tothe personalized EQ curve.

The example smoothing filter configurator 222 of the illustrated exampleof FIG. 2 defines parameters for smoothing between audio playbacksettings. For example, the smoothing filter configurator 222 can provideequations and/or parameters to implement smoothing (e.g., an exponentialsmoothing algorithm, a one-pole recursive smoothing filter, etc.) by theEQ adjustment implementor 220 when applying audio playback settings. Thesecond spectrogram 900 a of FIG. 9A illustrates the benefit ofimplementing the smoothing filter, displaying a spectrogram of an audiosignal which has undergone dynamic audio playback setting adjustmentusing a smoothing filter.

The example data store 224 of the illustrated example of FIG. 2 storesthe input media signal 202, an output model from the EQ neural network402 of FIG. 4, one or more profiles 229, EQ filter settings 209, EQinput feature sets 239, EQ gains/cuts 241, smoothing filter settings, anaudio signal buffer, and/or any other data associated with the dynamicplayback settings adjustment process implemented by the media unit 106.The data store 224 can be implemented by a volatile memory (e.g., aSynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM), etc.) and/ora non-volatile memory (e.g., flash memory, etc.). The data store 224 canadditionally or alternatively be implemented by one or more double datarate (DDR) memories, such as DDR, DDR2, DDR3, mobile DDR (mDDR), etc.The data store 224 can additionally or alternatively be implemented byone or more mass storage devices such as hard disk drive(s), compactdisk drive(s) digital versatile disk drive(s), etc. While, in theillustrated example, the data store 224 is illustrated as a singledatabase, the data store 224 can be implemented by any number and/ortype(s) of databases. Furthermore, the data stored in the data store 224can be in any data format such as, for example, binary data, commadelimited data, tab delimited data, structured query language (SQL)structures, etc.

The example update monitor 226 of the illustrated example monitors aduration between audio playback setting adjustments and determines whenan update duration threshold is satisfied. For example, the updatemonitor 226 can be configured with a one second update threshold,whereby the EQ model query generator 206 queries the EQ neural network(e.g., the EQ neural network 402 of FIG. 4) every one second todetermine new playback settings. In some examples, the update monitor226 communicates with the signal transformer 204 to simplify a sample(e.g., a three-second sample, a five-second sample, etc.) of the inputmedia signal 202 to initiate the process of determining updated audioplayback settings.

In operation, the signal transformer 204 accesses the input media signal202 and transforms the input audio signal into a frequency and/orcharacteristic form which is then utilized by the EQ model querygenerator 206 to query a neural network to determine EQ filter settings209. The neural network returns EQ filter settings 209 which areanalyzed and processed (e.g., converted into applicable filtercoefficients) by the EQ filter settings analyzer 208. The EQ filterselector 218 determines one or more of the filters represented by the EQsettings to apply to the input media signal 202. The EQ adjustmentimplementor 220 applies the selected filters using smoothing based onparameters from the smoothing filter configurator 222. The updatemonitor 226 monitors a duration since previous audio playback settingswere applied and updates the audio playback settings when an updateduration threshold is satisfied.

Second Implementation: Profile-Based Equalization

In a second implementation, the fingerprint generator 227 of theillustrated example of FIG. 2 generates identifiers (e.g., fingerprintsand/or signatures) for the input media signal 202 (e.g., content)received or accessed by the media device 102, the media device 104,and/or the media unit 106. For example, the fingerprint generator 227may include a reference fingerprint generator (e.g., a component thatcalculates a hash value from a portion of content) that is configured togenerate reference fingerprints or other identifiers of the input mediasignal 202 (e.g., received content), among other things. In someexamples, the fingerprint generator 227 implements the fingerprintgenerator 115 of FIG. 1.

The synchronizer 228 of the illustrated example of FIG. 2 synchronizesone or more profiles 229 from the content profile engine 116 to theinput media signal 202. In some examples, the media unit 106 can includea sequencer to order (or modify (e.g., adjust) the order in which) media(e.g., songs) is played. In additional or alternative examples, thesequencer can be external to the media unit 106.

In the example of FIG. 2, the synchronizer 228 may utilize a fingerprintor fingerprints associated with the input media signal 202 tosynchronize the input media signal 202 to the one or more profiles 229.For example, the one or more profiles 229 may include information thatrelates one or more settings to a known fingerprint for the input mediasignal 202 so that the synchronizer 228 can align the settings to aportion of the input media signal 202 in order to synchronize one of theone or more profiles 229 to the input media signal 202 during playbackof the input media signal 202.

In some examples, the synchronizer 228 can identify various audio oracoustic events (e.g., a snare hit, the beginning of a guitar solo, aninitial vocal) within the input media signal 202 and/or alternativerepresentations thereof, and align the one of the one or more profiles229 to the events within the input media signal 202 in order tosynchronize the one of the one or more profiles 229 to the input mediasignal 202 during playback of the input media signal 202. In additionalor alternative examples, the sequencer may organize the sequence ofsongs as part of an adaptive radio, a playlist recommendation, aplaylist of media (e.g., content) in the cloud (music and/or video) thatis specific to currently rendered media (e.g. content (e.g., using itsprofile)), user's profile, device settings that are known in advance toprovide personalized optimal experience, and so on.

In the second implementation, the example EQ personalization manager 210of the illustrated example of FIG. 2 generates personalized equalizationsettings (e.g., a personalized EQ setting, personalized EQ settings,curves, filter settings, etc.) which can be combined with the one ormore profiles 229 to account for personal preferences of a listener.

The device parameter analyzer 212 analyzes parameters associated withthe media unit 106 and/or a source device providing the input mediasignal 202. For example, the device parameter analyzer 212 can indicatean app from which the input media signal 202 originated. In some suchexamples, different apps may be associated with different equalizationprofiles. For example, an audio signal from an app associated withaudiobooks may have a different optimal equalization curve relative toan audio signal from an audio signal from an app associated withfitness.

In some examples, the device parameter analyzer 212 determines alocation of the device. For example, the device parameter analyzer 212can determine a location of the media unit 106 and/or the location of adevice providing the input media signal 202 to the media unit 106. Forexample, if the media unit 106 is integrated in a mobile device, and thelocation of the mobile device is a gym, a different personalizedequalization curve may be generated than if the mobile device is locatedat a user's home or workplace. In some examples, the device parameteranalyzer 212 determines whether the location of the mobile device iswithin a geofence of an area for which a personalized equalizationsetting (e.g., a personalized EQ setting) is determined (e.g., the gym,home, workplace, a library, etc.).

In some examples, the device parameter analyzer 212 determines a user ofthe media unit 106 and/or a user of a device supplying the input mediasignal 202 to the media unit. For example, if the media unit 106 isintegrated into a mobile device, the device parameter analyzer 212 maydetermine a user of the mobile device based on a login associated withthe user device and/or another identifier associated with the userdevice. In some examples, a user may be asked to select a user profileto indicate who is utilizing the mobile device and/or other deviceassociated with the media unit 106.

The device parameter analyzer 212 of the illustrated example outputsand/or adjusts a personalized EQ curve based on any parameters which thedevice parameter analyzer 212 is able to access (e.g., a location, auser identifier, a source identifier, etc.).

The historical EQ manager 214 of the illustrated example of FIG. 2maintains historical data pertaining to past equalization curvesutilized to enable subsequent personalized EQ curve adjustments. Forexample, if a user frequently listens to rock music, and frequentlyutilizes EQ curves which are most suitable for rock music, thehistorical EQ manager 214 can help adjust and/or generate a personalizedEQ curve based on the user's typical music preferences. For example, thehistorical EQ manager 214 can generate a personalized EQ curve based ona defined historical listening period. For example, the historical EQmanager 214 can generate a personalized EQ curve based on 1 hour ofprevious listening, based the past 24 hours of listening, and/or for anyother time period. Stated differently, the historical EQ manager 214 cangenerate and/or adjust a personalized EQ curve based on EQ settingsassociated with a previous period of time. The historical EQ manager 214takes the one or more profiles 229 that are being generated in real timeand adds those settings for each band (e.g., each of the five bands) ofEQ into a long term, personalized EQ profile that averages the EQsettings for the historical period. The average curve that the systemhas seen for the historical period becomes the personalization EQ curve.This curve will reflect the average EQ of the type of music that theuser has been listening to. For instance, if a user has been listeningto Heavy Metal for the past 60 minutes, that user will have a differentEQ curve stored in his/her user profile than if that user had beenlistening to Top 40 Pop for the past 60 minutes.

The averaging operation could be a rolling average, an IIR filter, (allpole filter) with the coefficients set to average over the time period,or any other averaging technique. This averaging can alleviate the needto hold a long duration of buffer information. By utilizing historicalEQ data, the EQ settings can be made to have a degree of “stickiness,”whereby the system gradually learns a listener's preference over timeand provides more useful equalization curves.

In some examples, the historical EQ manager 214 determines a smallsubset of genres that could be used with a table look up for a given EQcurve for each genre (Rock, Country, Spoken, Hip Hop, etc.). Based onthis subset of genres, a user can EQ curves can be generated, adjusted,or selected.

The user input analyzer 216 of the illustrated example of FIG. 2accesses and responds to user inputs corresponding to equalizationsettings. For example, a user may provide inputs as to whether aspecific equalization setting is preferred (e.g., by pressing a “like”button, by providing a user rating, etc.). These inputs can then beutilized when generating the personalized EQ curve to more heavilyweight those equalization settings which a user indicated they prefer.In some examples, user preferences are stored for a defined period(e.g., a few months, a year, etc.). In some examples, user preferencesare stored in associated with particular user accounts (e.g., userlogins identified by the device parameter analyzer 212). In someexamples, the user input analyzer 216 receives “reset” signals from alistener, which indicate that the user would like to undo any automatedpersonalized equalization which is being applied to the audio signal. Insome examples, the user input analyzer 216 adjusts a strength of theequalization based on a strength input from the listener.

In the second implementation, the EQ adjustment implementor 220 isconfigured to modify playback of the input media signal 202 based on oneor more profiles 229 for the input media signal 202. In such additionalor alternative examples, the EQ adjustment implementor 220 implements anadjustor to modify playback in the input media signal 202 based on theone or more profiles 229. For example, the EQ adjustment implementor 220can apply information within the one or more profiles 229 to modify oradjust the settings of an equalizer and or a dynamic processor of themedia unit 106, the media device 102, and/or the media device 104, inorder to adjust and/or tune the equalization during the playback of theinput media signal 202 (e.g., the stream of content). Stateddifferently, the one or more profiles 229 include information to causethe EQ adjustment implementor 220 to adjust equalization of a portion ofthe input media signal 202. When the media (e.g., content) is video, theone or more profiles 229 may be used to adjust video settings such ascolor temperature, dynamic range, color palette, brightness, sharpness,or any other video-related settings.

In addition to the equalization, the EQ adjustment implementor 220 canadjust a variety of different playback settings, such as equalizationsettings, virtualization settings, spatialization settings, and so on.For example, the EQ adjustment implementor 220 may access informationidentifying a genre assigned to the input media signal 202 (e.g., streamof content) and modify playback of the input media signal 202 (e.g.,stream of content) by adjusting equalization settings of a playbackdevice to settings associated with the identified genre. As anotherexample, the EQ adjustment implementor 220 may access informationidentifying signal strength parameters for different frequencies of thestream of content and modify playback of the stream of content byadjusting equalization settings of a playback device to settings usingthe signal strength parameters.

In some examples of the second implementation, the EQ adjustmentimplementor 220 blends between the one or more profiles 229 generated bythe content profile engine 116 and a personalized EQ from the EQpersonalization manager 210. For example, a user profile EQ curve can beblended with the real time profiles. In some examples, a weight is usedto blend the personalized EQ curve and the one or more profiles 229;multiple weights may be used as well. As an example, a final EQ curvethat the shapes the audio that the user ends up listening to may be 0.5times the current EQ based on dynamically generated filter settings andis 0.5 times the personalized EQ curve. As another example, the firstnumber could be 0.25 to the current EQ based on dynamically generatedfilter settings and 0.75 to the personalized EQ curve.

Third Implementation: Thresholding-Based Equalization

In the third implementation, the example buffer manager 230 of theillustrated example of FIG. 2 receives the input media signal 202 andstores a portion of the input media signal 202 in the data store 224.The buffer manager 230 can configure the buffer (e.g., the portion ofthe input media signal 202) to be any duration (e.g., ten seconds,thirty seconds, one minute, etc.). The portion of the input media signal202 that is stored in the buffer in the data store 224 is utilized todetermine equalization features, thereby enabling the equalizationfeatures to be representative of a longer duration of the input mediasignal 202 than if the features were generated based on instantaneouscharacteristics of the input media signal 202. The duration of thebuffer may be tuned based on how responsive the equalization should be.For example, a very brief buffer duration may result in drastic changesin the equalization curve when spectral characteristics of the inputmedia signal 202 change (e.g., during different portions of a song),while a long buffer period averages out these large changes in the inputmedia signal 202 and provides more consistent equalization profiles. Thebuffer manager 230 can cause portions of the input media signal 202which are no longer within the buffer period to be discarded. Forexample, if the buffer period is ten seconds, once a portion of theinput media 202 has been in the buffer for ten seconds, this portionwill be removed.

In some examples, a neural network is utilized to identify media changes(e.g., track changes, changes in the media source, etc.) and the outputis utilized to adjust the equalization in response to the change inmedia. For example, when a new track is detected by the neural network,short-term instantaneous or average volume (e.g., volume values atfrequency ranges throughout a period shorter than the standard bufferperiod, standard deviation values at frequency ranges throughout theshorter period, etc.) can be calculated to cause a quick adjustment inthe EQ input feature set 239 and consequently in the EQ gains/cuts 241received from the EQ neural network 402 of FIG. 4 (e.g., equalizationadjustments output from the EQ neural network 402). In some examples,in-between media changes, a longer volume averaging technique isutilized (e.g., determining equalization profiles based on 30-secondvolume averages, determining equalization profiles based on 45-secondvolume averages, etc.) to avoid rapid fluctuations in the equalizationprofile throughout a track.

In some examples, in addition to or alternatively to utilizing a neuralnetwork to identify media changes, a hysteresis-based logic can beimplemented to cause faster equalization changes when a more drasticchange in characteristics of media represented in the input media signal202 occurs (e.g., a transition from bass-heavy to treble-heavy media).

In some examples, the media unit 106 can detect a change in source ofthe input audio signals and trigger a short term equalization update asdescribed above (e.g., calculating short-term instantaneous or averagevolume and determining an equalization profile based on these changes)to account for differences in the media from the new source relative tothe prior source.

The example time to frequency domain converter 232 of the illustratedexample of FIG. 2 converts the input media signal 202 from a time-domainrepresentation to a frequency-domain representation. In some examples,the time to frequency domain converter 232 utilizes a Fast FourierTransform (FFT). In some examples, the time to frequency domainconverter 232 converts the input media signal 202 into a linear-spacedand/or log-spaced frequency domain representation. The time to frequencydomain converter 232 may utilize any type of transform (e.g., ashort-time Fourier Transform, a Constant-Q transform, Hartley transform,etc.) to convert the input media signal 202 from a time-domainrepresentation to a frequency-domain representation. In some examples,the media unit 106 may alternatively perform the audio equalizationtechniques disclosed herein in the time-domain.

The example volume calculator 234 of the illustrated example of FIG. 2calculates volume levels at frequency ranges for the input media signal202. In some examples, the volume calculator 234 calculates an averagevolume level throughout the buffer duration (e.g., ten seconds, thirtyseconds, etc.) for frequency bins (e.g., frequency range) in thelinear-spaced frequency representation of the input media signal 202(e.g., an average volume representation). The volume calculator 234 ofthe illustrated example of FIG. 2 generates a frequency representationof the average volume of the portion of the input media signal 202 thatis stored in the buffer. Additionally or alternatively, the volumecalculator 234 of the illustrated example of FIG. 2 calculates astandard deviation throughout the buffer duration for the frequencybins. In some examples, the volume calculator 2342 calculates the volumelevels for log-spaced frequency bins (e.g., critical frequency bands,Bark bands, etc.). In some examples, to calculate the average volumelevel for frequency bins, the volume calculator 234 converts thefrequency representation of the input media signal 202 to real values.

The example energy calculator 236 of the illustrated example of FIG. 2calculates energy values for media signals (e.g., audio signals). Insome examples, the energy calculator 236 calculates a root means square(RMS) value of the frequency representation of the audio signal prior toequalization (e.g., based on a frequency representation of the inputmedia signal 202 stored in the buffer) and after an equalization curveis applied (e.g., after the EQ curve generator 240 has appliedequalization gains/cuts to the average frequency representation of theaudio signal). In some examples, the energy calculator 236 calculatesthe energy of a single frequency representation of the input mediasignal 202 (e.g., based on volume levels of any instant throughout thebuffer period), and/or calculates the energy of the average frequencyrepresentation of the input media signal 202 throughout the bufferperiod.

In some examples, the energy calculator 236 communicates energy valuesbefore and after equalization to the volume normalizer 248, to enablenormalization of the volume and avoid perceptible changes in the overallvolume after equalization. The energy calculator 236 of the illustratedexample of FIG. 2 calculates the energy of the equalized averagefrequency representation.

The example input feature set generator 238 of the illustrated exampleof FIG. 2 generates features (e.g., audio features) corresponding to theinput media signal 202 to input to the EQ neural network 402 of FIG. 4.In some examples, the input feature set generator 238 generates a setincluding average volume measurements for the frequency bins of thefrequency representation of the input media signal 202 throughout thebuffer period and/or average standard deviation measurements forfrequency bins of the frequency representation of the input media signal202 throughout the buffer period. In some examples, the input featureset generator 238 may include any available metadata in the set that isdelivered to the EQ neural network 402 of FIG. 4 to assist the EQ neuralnetwork 402 of FIG. 4 in determining the appropriate equalizationsettings to be utilized for the input media signal 202.

The example EQ curve manager 240 of the illustrated example of FIG. 2determines equalization curves to be utilized to equalize the inputmedia signal 202. The example EQ curve manager 240 includes the examplevolume adjuster 242, the example thresholding controller 244, and theexample EQ curve generator 246.

The example volume adjuster 242 of the illustrated example of FIG. 2receives the EQ gains/cuts 241 and makes volume adjustments at frequencyranges of the average representation of the input media signal 202. Insome examples, the volume adjuster 242 receives the EQ gains/cuts 241 asa plurality of values (e.g., scalars) to be applied at specificfrequency ranges of the audio signal. In other examples, these valuescan be log-based gains and cuts (e.g., in decibels). In some suchexamples, the EQ gains/cuts 241 correspond to a plurality of log-spacedfrequency bins. For example, the EQ gains/cuts 241 may correspond to the25 critical bands used in the Bark Band representation.

In some examples, to apply the EQ gains/cuts 241 to the buffered portionof the input media signal 202, the volume adjuster 242 converts thelinearly-spaced frequency representation of the input media signal 202(e.g., as generated by the time to frequency domain converter 232) to alog-spaced frequency representation of the input media signal 202. Insome such examples, the volume adjuster 242 can add the EQ gains/cuts241 in decibels to the volume levels in the log-spaced frequencyrepresentation to generate an equalized log-spaced frequency version ofthe buffered portion of the input media signal 202. The volume adjuster242 of the illustrated example communicates the equalized log-spacedfrequency version of the buffered portion of the input media signal 202to the thresholding controller 244. In some examples, the EQ gains/cuts241 may be provided in a linear-spaced frequency representation and/orother representation, and applied to a common (i.e., linear-spaced)representation of the buffered portion of the input media signal 202.

In some examples, the volume adjuster 242 accesses information regardingtechnical limitations of the source of the input media signal 202 and/orother technical characteristics pertaining to the input media signal 202and utilizes these technical limitations or characteristics to refinewhich frequency ranges experience changes in volume. For example, thevolume adjuster 242 can access information pertaining to a type ofencoding of the input media signal 202 (e.g., as determined by a decoderin the media unit 106, as determined by analyzing the input media signal202 for encoding artifacts, etc.). In some such examples, the volumeadjuster 242 can prevent volume adjustments that may have a negativeeffective on the quality of the audio signal (e.g., adjustments whichboost volume in frequency ranges including encoding artifacts).

The example thresholding controller 244 of the illustrated example ofFIG. 2 performs techniques to smooth out the equalized version (e.g.,from the volume adjuster 242) of the buffered portion of the input mediasignal 202. In some examples, after the volume adjuster 242 applies theEQ gains/cuts 241 to the buffered portion of the input media signal 202,a frequency representation of the equalized audio signal may havelocalized outliers (e.g., irregularities appearing as or short-termpeaks or dips on a frequency-volume plot of the equalized audio signal)that may result in perceptible artifacts in the equalized audio signal.As utilized herein, the term localized outlier refers to an irregularityon a frequency-volume plot of an equalized audio signal, such as a largedifference in volume between adjacent frequency values. In someexamples, localized outliers are detected by determining whether thesecond derivative of volume over a frequency range exceeds a threshold.

The thresholding controller 244 of the illustrated example of FIG. 2selects a plurality of frequency values at which to initiate thethresholding technique. The thresholding controller 244 determinesvolume levels at the plurality of frequency values, and then calculatesa measure of the difference between these frequency values. In someexamples, the thresholding controller 244 calculates the secondderivative of the volume values over the plurality of frequency values.As an example, if three frequency values are being analyzed to determinewhether a central one of the three frequency values corresponds to alocalized outlier (e.g., an irregularity), the following equation may beutilized to calculate the second derivative, where the array val[ ]includes the volume values, the index “i” corresponds to the frequencyvalue index:

|(val[i−2]−(2(val[i−1])+val[i])|   Equation 1

The thresholding controller 244 can compare the output of Equation 1 toa threshold. In some examples, if the output of Equation 1, or any otherequation utilized to calculate the relative difference of volume at oneof the frequency values to volumes at adjacent frequency values,satisfies a threshold (e.g., exceeds the threshold), a smoothingcalculation may be utilized to remove the irregularity. In someexamples, the thresholding controller 244 adjusts the volume level at adetected irregularity by changing the volume to a midpoint betweenvolume levels at adjacent frequency values. FIG. 17B illustrates anexample of utilizing this midpoint volume adjustment on a localizedoutlier illustrated in the equalized audio signal represented in FIG.17A. In some examples, the thresholding controller 244 may utilize anyother technique to change the volume at a detected localized outlier.For example, the thresholding controller 244 may set the volume at thedetected localized outlier equal to the volume at an adjacent frequencyvalue or some other value to attempt to remove the localized outlier.

In some examples, the thresholding controller 244 iteratively movesthroughout frequency ranges of the equalized audio signal to identifyany volume levels which represents irregularities. In some examples, thethresholding controller 244, after analyzing all of the frequencyvalues/ranges of the equalized audio signal, may iterate throughout theequalized audio signal one or more additional times to determine whetherany localized outliers remain after the first adjustment phase (e.g.,after volume levels for the localized outliers detected were changed).In some examples, the thresholding controller 244 is a neural networkand/or other artificial intelligence that has been trained forirregularity (e.g., anomaly) detection. In some such examples, thethresholding controller 244 may eliminate the irregularities in oneadjustment, without additional iterations being necessary.

After the thresholding controller 244 has removed the localized outliersfrom the equalized frequency representation of the audio signal, or onceanother stopping condition has been reached (e.g., performing teniterations of localized outlier detection and adjustment throughout theentire frequency range), the thresholding controller 244 can communicatethe final equalized representation of the audio signal to the EQ curvegenerator 246 so that the EQ curve generator 246 can determine anequalization curve to apply to the input media signal 202.

The EQ curve generator 246 of the illustrated example of FIG. 2determines a final equalization curve to apply to the buffered portionof the input media signal 202. In some examples, the EQ curve generator246 of the illustrated example of FIG. 2 subtracts the original averagelog-spaced frequency representation of the buffered portion of the inputmedia signal 202 from the equalized version that is output from thethresholding controller 244 to determine the final equalization curve toutilize for equalization. In some such examples, after this subtraction,the EQ curve generator 246 converts the final equalization curve to aform that can be applied to the frequency-domain representation of thebuffered audio signal (e.g., a linear-spaced form). In some suchexamples, the EQ curve generator 246 of the illustrated example thenapplies the final EQ curve (e.g., the linear-spaced frequencyrepresentation of the final EQ curve) to the correspondingrepresentation (e.g., the linear-spaced frequency representation of thebuffered audio signal). The EQ curve generator 246 may communicate theresulting equalized audio signal to the energy calculator 236, thevolume normalizer 248, and/or the frequency to time domain converter250. As used herein, an EQ curve includes gains/cuts and/or other volumeadjustments corresponding to frequency ranges of an audio signal.

The example volume normalizer 248 of the illustrated example of FIG. 2accesses an indication of the change in energy levels before and afterequalization of the input media signal 202. The volume normalizer 248 ofthe illustrated example of FIG. 2 performs volume normalization toaccount for the overall change of the audio signal before and afterequalization. In some examples, if the change in energy level before andafter the equalization of the input media signal 202 exceeds athreshold, the volume normalizer 248 applies a scalar volume adjustmentto account for the change in energy level. In some examples, the volumenormalizer 248 may utilize a dynamic range compressor. In some examples,the energy calculator 236 may calculate a ratio of the energy before andafter the equalization process, and the volume normalizer 248 canutilize this ratio to cancel out this change in overall volume. Forexample, if the overall energy of the audio portion of the input mediasignal 202 doubled, the volume normalizer 248 can apply an overallvolume cut to reduce the volume by one-half. In some examples, thevolume normalizer 248 may determine that the change in energy isinsufficient to justify a volume normalization. The volume normalizer248 of the illustrated example communicates the final (volume adjusted,if applicable) equalized audio signal to the frequency to time domainconverter 250.

The frequency to time domain converter 250 of the illustrated example ofFIG. 2 converts the final equalized audio signal from the frequencydomain to the time domain to ultimately be output from the media unit106.

While an example manner of implementing the media unit 106 of FIG. 1 isillustrated in FIG. 2, one or more of the elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample signal transformer 204, the example EQ model query generator206, the example EQ filter settings analyzer 208, the example EQpersonalization manager 210, the example device parameter analyzer 212,the example historical EQ manager 214, the example user input analyzer216, the example EQ filter selector 218, the example EQ adjustmentimplementor 220, the example smoothing filter configurator 222, theexample data store 224, the example update monitor 226, the examplefingerprint generator 227, the example synchronizer 228, the examplebuffer manager 230, the example time to frequency domain converter 232,the example volume calculator 234, the example energy calculator 236,the example input feature set generator 238, the example EQ manager 240,the example volume adjuster 242, the example thresholding controller244, the example EQ curve generator 246, the example volume normalizer248, and/or the example frequency to time domain converter 250 and/or,more generally, the example media unit 106 of FIG. 2 may be implementedby hardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example signaltransformer 204, the example EQ model query generator 206, the exampleEQ filter settings analyzer 208, the example EQ personalization manager210, the example device parameter analyzer 212, the example historicalEQ manager 214, the example user input analyzer 216, the example EQfilter selector 218, the example EQ adjustment implementor 220, theexample smoothing filter configurator 222, the example data store 224,the example update monitor 226, the example fingerprint generator 227,the example synchronizer 228, the example buffer manager 230, theexample time to frequency domain converter 232, the example volumecalculator 234, the example energy calculator 236, the example inputfeature set generator 238, the example EQ manager 240, the examplevolume adjuster 242, the example thresholding controller 244, theexample EQ curve generator 246, the example volume normalizer 248,and/or the example frequency to time domain converter 250 and/or, moregenerally, the example media unit 106 of FIG. 2 could be implemented byone or more analog or digital circuit(s), logic circuits, programmableprocessor(s), programmable controller(s), graphics processing unit(s)(GPU(s)), digital signal processor(s) (DSP(s)), application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s))and/or field programmable logic device(s) (FPLD(s)). When reading any ofthe apparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example signaltransformer 204, the example EQ model query generator 206, the exampleEQ filter settings analyzer 208, the example EQ personalization manager210, the example device parameter analyzer 212, the example historicalEQ manager 214, the example user input analyzer 216, the example EQfilter selector 218, the example EQ adjustment implementor 220, theexample smoothing filter configurator 222, the example data store 224,the example update monitor 226, the example fingerprint generator 227,the example synchronizer 228, the example buffer manager 230, theexample time to frequency domain converter 232, the example volumecalculator 234, the example energy calculator 236, the example inputfeature set generator 238, the example EQ manager 240, the examplevolume adjuster 242, the example thresholding controller 244, theexample EQ curve generator 246, the example volume normalizer 248,and/or the example frequency to time domain converter 250 and/or, moregenerally, the example media unit 106 of FIG. 2 is/are hereby expresslydefined to include a non-transitory computer readable storage device orstorage disk such as a memory, a digital versatile disk (DVD), a compactdisk (CD), a Blu-ray disk, etc. including the software and/or firmware.Further still, the example media unit 106 of FIG. 1 may include one ormore elements, processes and/or devices in addition to, or instead of,those illustrated in FIG. 2, and/or may include more than one of any orall of the illustrated elements, processes and devices. As used herein,the phrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

FIG. 3 is a block diagram showing additional detail of the contentprofile engine 116 of FIG. 1 according to the second implementation. Theexample content profile engine 116 includes an example content retriever302, an example fingerprint generator 304, an example content identifier306, an example profiler 308, and an example profile data store 310. Asdescribed herein, in some examples, the systems and methods identifymedia (e.g., content) to be streamed or otherwise transmitted to themedia device 102, the media device 104, and/or the media unit 106 andgenerate and/or determine a profile to deliver to the media device 102,the media device 104, and/or the media unit 106 that providesinformation associated with a mood, style, or other attributes of thecontent. In some examples, the profile may be an identifier thatidentifies a content type. For example, the profile may identify themedia (e.g., content) as news, an action movie, a sports event, or thelike. Different settings on a TV may then be adjusted in real-time(e.g., on-the-fly) based on the profile. Similarly, the profile mayidentify a radio talk show, a song, a jingle, a song genre, or the like.Accordingly, audio settings may then be adjusted in real-time (e.g.,on-the-fly) to enhance the audio delivered to a listener.

In the example of FIG. 3, the content retriever 302 accesses and/orotherwise retrieves the input media signal 202 prior to delivery to themedia unit 106 (e.g., a stream of content to be delivered to a playbackdevice (e.g., the media device 102, the media device 104, the media unit106, etc.)). For example, the content retriever 302 may access the inputmedia signal 202 from the content provider 114 that is providing theinput media signal 202 (e.g., the stream of content) to the playbackdevice (e.g., the media device 102, the media device 104, the media unit106, etc.) over the network 112. As another example, the contentretriever 302 may access the input media signal 202 (e.g., the stream ofcontent) from the content provider 114 that is locally stored by theplayback device (e.g., the media device 102, the media device 104, themedia unit 106, etc.).

In the example of FIG. 3, the content retriever 302 may access varioustypes of media (e.g., various types of content streams), such as audiocontent streams, video streams, and so on. For example, the contentretriever 302 may access a stream of songs or other music, a stream ofspoken content, a podcast, YouTube™ videos and clips, and so on.

The fingerprint generator 304 of the illustrated example of FIG. 3generates identifiers (e.g., fingerprints and/or signatures) for theinput media signal 202 (e.g., content) received or accessed by thecontent profile engine 116. For example, the fingerprint generator 304may include a reference fingerprint generator (e.g., a component thatcalculates a hash value from a portion of content) that is configured togenerate reference fingerprints or other identifiers of the input mediasignal 202 (e.g., received content), among other things. In someexamples, the fingerprint generator 304 implements the fingerprintgenerator 115 of FIG. 1.

In the illustrated example of FIG. 3, the content identifier 306identifies a portion of media (e.g., a portion of content) within theinput media signal 202 (e.g., the stream of content) to be delivered tothe playback device (e.g., the media device 102, the media device 104,the media unit 106, etc.). The content identifier 306 may identify theportion of media (e.g., portion of content) via a variety of processes,including a comparison of a fingerprint of the input media signal 202(e.g., content) to reference fingerprints of known media (e.g.,content), such as reference fingerprints generated by the fingerprintgenerator 304. For example, the content identifier 306 may generateand/or access query fingerprints for a frame or block of frames of theportion of the input media signal 202 or the input media signal 202, andperform a comparison of the query fingerprints to the referencefingerprints in order to identify the piece of content or stream ofcontent associated with the input media signal 202.

In the example illustrated in FIG. 3, the profiler 308 determines one ormore profiles 229 for the identified piece or a segment/portion withinthe input media signal 202 (e.g., the stream content) and/delivers theone or more profiles 229 to the playback device (e.g., the media device102, the media device 104, the media unit 106, etc.). For example, theprofiler 308 can determine one or more characteristics for the inputmedia signal 202 and/or determine one or more characteristics formultiple portions of the input media signal 202, such as frames orblocks of frames of the input media signal 202. In some examples, theprofiler 308 stores the one or more profiles 229 in the profile datastore 310.

The example profiler 308 may render, generate, create, and/or otherwisedetermine the one or more profiles 229 for the input media signal 202,such as audio content, having a variety of different characteristics.For example, the one or more profiles 229 may include characteristicsassociated with EQ settings, such as different audio frequencies withinthe audio content. The one or more profiles 229 may include differenttypes of information. Example profile information may include: (1)information identifying a category associated with the song, such as acategory for a style of music (e.g., rock, classical, hip-hop,instrumental, spoken-word, jingle and so on); (2) informationidentifying a category associated with a video segment, such as style ofvideo (e.g. drama, sci-fi, horror, romance, news, TV show, documentary,advertisement, and so on); (3) information identifying a mood associatedwith the song or video clip, such as upbeat mood, a relaxed mood, a softmood, and so on; (4) information identifying signal strength parametersfor different frequencies within the content, such as low frequenciesfor bass and other similar tones, high frequencies for spoken or sungtones; and/or (5) information identifying color palette, brightness,sharpness, motion, blurriness, presence of text and/or subtitles orclose caption, specific content with said text or subtitles, scene cuts,black frames, presence of display format adjustment bars/pillars,presence or absence of faces, landscapes, or other objects, presence ofspecific company, network, or broadcast logos, and so on.

Therefore, the one or more profiles 229 may represent the playbackattributes (e.g., “DNA”) of the input media signal 202, which may beused by the media unit 106 to control the playback device (e.g., themedia device 102, the media device 104, the media unit 106, etc.) inorder to optimize or enhance the experience during playback of the inputmedia signal 202, among other things. As illustrated in FIG. 3, thecontent profile engine 116 may generate and deliver the one or moreprofiles 229 to the media unit 106, which adjusts the playback settingsof the playback device (e.g., the media device 102, the media device104, the media unit 106, etc.) during playback of the input media signal202 (e.g., the stream of content), among other things.

In the example of FIG. 3, the profile data store 310 stores one or moreprofiles, one or more reference fingerprints, and/or any other dataassociated with the dynamic playback settings adjustment processimplemented by the media unit 106 via the one or more profiles 229. Theprofile data store 310 can be implemented by a volatile memory (e.g., aSynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM), etc.) and/ora non-volatile memory (e.g., flash memory, etc.). The profile data store310 can additionally or alternatively be implemented by one or moredouble data rate (DDR) memories, such as DDR, DDR2, DDR3, mobile DDR(mDDR), etc. The profile data store 310 can additionally oralternatively be implemented by one or more mass storage devices such ashard disk drive(s), compact disk drive(s) digital versatile diskdrive(s), etc. While, in the illustrated example, the profile data store310 is illustrated as a single database, the profile data store 310 canbe implemented by any number and/or type(s) of databases. Furthermore,the data stored in the profile data store 310 can be in any data formatsuch as, for example, binary data, comma delimited data, tab delimiteddata, structured query language (SQL) structures, etc.

While an example manner of implementing the content profile engine 116of FIG. 1 is illustrated in FIG. 3, one or more of the elements,processes and/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example content retriever 302, the example fingerprintgenerator 304, the example content identifier 306, the example profiler308, the example profile data store 310 and/or, more generally, theexample content profile engine 116 of FIG. 3 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example contentretriever 302, the example fingerprint generator 304, the examplecontent identifier 306, the example profiler 308, the example profiledata store 310, and/or, more generally, the example content profileengine 116 of FIG. 3 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),programmable controller(s), graphics processing unit(s) (GPU(s)),digital signal processor(s) (DSP(s)), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example contentretriever 302, the example fingerprint generator 304, the examplecontent identifier 306, the example profiler 308, the example profiledata store 310, and/or, more generally, the example content profileengine 116 of FIG. 3 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample content profile engine 116 of FIG. 3 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 3, and/or may include more than one of any or all ofthe illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

FIG. 4 is a block diagram showing additional detail of the audio EQengine 118 of FIG. 1. The example audio EQ engine 118 is configured tooperate according to at least two implementations. In some examples, thetrained model resides on the audio EQ engine 118 (e.g., in the EQ neuralnetwork 402), while in some examples the trained model is exported fordirect use on the media unit 106.

Machine learning techniques, whether deep learning networks or otherexperiential/observational learning system, can be used to optimizeresults, locate an object in an image, understand speech and convertspeech into text, and improve the relevance of search engine results,for example. While many machine learning systems are seeded with initialfeatures and/or network weights to be modified through learning andupdating of the machine learning network, a deep learning network trainsitself to identify “good” features for analysis. Using a multilayeredarchitecture, machines employing deep learning techniques can processraw data better than machines using conventional machine learningtechniques. Examining data for groups of highly correlated values ordistinctive themes is facilitated using different layers of evaluationor abstraction.

Machine learning techniques, whether neural networks, deep learningnetworks, and/or other experiential/observational learning system(s),can be used to generate optimal results, locate an object in an image,understand speech and convert speech into text, and improve therelevance of search engine results, for example. Deep learning is asubset of machine learning that uses a set of algorithms to modelhigh-level abstractions in data using a deep graph with multipleprocessing layers including linear and non-linear transformations. Whilemany machine learning systems are seeded with initial features and/ornetwork weights to be modified through learning and updating of themachine learning network, a deep learning network trains itself toidentify “good” features for analysis. Using a multilayeredarchitecture, machines employing deep learning techniques can processraw data better than machines using conventional machine learningtechniques. Examining data for groups of highly correlated values ordistinctive themes is facilitated using different layers of evaluationor abstraction.

For example, deep learning that utilizes a convolutional neural network(CNN) segments data using convolutional filters to locate and identifylearned, observable features in the data. Each filter or layer of theCNN architecture transforms the input data to increase the selectivityand invariance of the data. This abstraction of the data allows themachine to focus on the features in the data it is attempting toclassify and ignore irrelevant background information.

Deep learning operates on the understanding that many datasets includehigh level features which include low level features. While examining animage, for example, rather than looking for an object, it is moreefficient to look for edges which form motifs which form parts, whichform the object being sought. These hierarchies of features can be foundin many different forms of data.

Learned observable features include objects and quantifiableregularities learned by the machine during supervised learning. Amachine provided with a large set of well classified data is betterequipped to distinguish and extract the features pertinent to successfulclassification of new data.

A deep learning machine that utilizes transfer learning can properlyconnect data features to certain classifications affirmed by a humanexpert. Conversely, the same machine can, when informed of an incorrectclassification by a human expert, update the parameters forclassification. Settings and/or other configuration information, forexample, can be guided by learned use of settings and/or otherconfiguration information, and, as a system is used more (e.g.,repeatedly and/or by multiple users), a number of variations and/orother possibilities for settings and/or other configuration informationcan be reduced for a given situation.

An example deep learning neural network can be trained on a set ofexpert classified data, for example. This set of data builds the firstparameters for the neural network, and this would be the stage ofsupervised learning. During the stage of supervised learning, the neuralnetwork can be tested whether the desired behavior has been achieved.Example flowcharts representative of machine readable instructions fortraining the EQ neural network 402 is illustrated and described inconnection with FIGS. 7 and 16.

First Implementation: Filter-Based Equalization

In the first implementation, the example EQ neural network 402 of theillustrated example can be trained using a library of reference audiosignals for which audio playback settings have been specificallytailored and optimized (e.g., by an audio engineering). In someexamples, the EQ neural network 402 is trained by associating samples ofones of the reference audio signals (e.g., training data 408) with theknown audio playback settings for the reference audio signals. Forexample, gain, frequency, and/or Q values for one or more filters thatare recommended to be applied to the track can be associated withindividual audio signal samples of the track, thus training the EQneural network 402 to associate similar audio samples with the optimizedplayback settings (e.g., the gain, frequency, and/or Q values for one ormore recommended filters). In some examples, various biases associatedwith different playback settings can be indicated as well. For example,if a first ten tracks are utilized for training and audio playbacksettings (e.g., EQ parameters corresponding to audio playback settings)for the first ten tracks were determined by a first engineer, and asecond ten tracks are utilized for training and audio playback settingsfor the second ten tracks were determined by a second engineer, the EQneural network 402 may additionally be trained to learn differentpreferences and/or biases associated with the first and second audioengineers and mitigate these to generate a more objective model.

In some examples, a loss function can be utilized for training the EQneural network 402. For example, Equation 2, represents one example lossfunction that can be utilized, where f corresponds to frequency inHertz, g corresponds to gain in Decibels, and q corresponds to the Qfactor (unitless):

(g,f,q)∝∥g−g∥₂ ²+∥q−q∥₂ ²+∥log₁₀(f)−log₁₀(f)∥₂ ²   Equation 2

Once a desired neural network behavior has been achieved (e.g., amachine has been trained to operate according to a specified threshold,etc.), the neural network can be deployed for use (e.g., testing themachine with “real” data, etc.). During operation, neural networkclassifications can be confirmed or denied (e.g., by an expert user,expert system, reference database, etc.) to continue to improve neuralnetwork behavior. The example neural network is then in a state oftransfer learning, as parameters for classification that determineneural network behavior are updated based on ongoing interactions. Incertain examples, the neural network such as the EQ neural network 402can provide direct feedback to another process, such as an audio EQscoring engine 404, etc. In certain examples, the EQ neural network 402outputs data that is buffered (e.g., via the cloud, etc.) and validated(e.g., via EQ validation data 410) before it is provided to anotherprocess.

In the example of FIG. 4, the EQ neural network 402 receives input fromprevious outcome data associated with audio playback settings trainingdata, and outputs an algorithm to predict audio playback settingsassociated with audio signals. The EQ neural network 402 can be seededwith some initial correlations and can then learn from ongoingexperience. In some examples, the EQ neural network 402 continuallyreceives feedback from at least one audio playback settings trainingdata. In the example of FIG. 4, throughout the operational life of theaudio EQ engine 118, the EQ neural network 402 is continuously trainedvia feedback and the example audio EQ engine validator 406 can beupdated based on the EQ neural network 402 and/or additional audioplayback settings training data 408 as desired. The EQ neural network402 can learn and evolve based on role, location, situation, etc.

In some examples, a level of accuracy of the model generated by the EQneural network 402 can be determined by an example audio EQ enginevalidator 406. In such examples, at least one of the audio EQ scoringengine 404 and the audio EQ engine validator 406 receive a set of audioplayback settings validation data 410. Further in such examples, theaudio EQ scoring engine 404 receives inputs (e.g., CQT data) associatedwith the audio playback settings validation data 410 and predicts one ormore audio playback settings associated with the inputs. The predictedoutcomes are distributed to the audio EQ engine validator 406. The audioEQ engine validator 406 additionally receives known audio playbacksettings associated with the inputs and compares the known audioplayback settings with the predicted audio playback settings receivedfrom the audio EQ scoring engine 404. In some examples, the comparisonwill yield a level of accuracy of the model generated by the EQ neuralnetwork 402 (e.g., if 95 comparison yield a match and 5 yield an error,the model is 95% accurate, etc.). Once the EQ neural network 402 reachesa desired level of accuracy (e.g., the EQ neural network 402 is trainedand ready for deployment), the audio EQ engine validator 406 can outputthe model (e.g., the output 414) to the data store 224 of FIG. 2 for useby the media unit 106 to determine audio playback settings. In someexamples, after being trained, the EQ neural network 402 outputssufficiently accurate EQ filter settings (e.g., EQ filter settings 209)to the media unit 106.

Third Implementation: Thresholding-Based Equalization

In the third implementation, the example EQ neural network 402 of theillustrated example can be trained using a library of reference audiosignals for which audio equalization profiles (e.g., gains, cuts, etc.)have been determined (e.g., by an audio engineer). In the illustratedexample of FIG. 4, the EQ neural network 402 receives the exampletraining data 408 (e.g., reference audio signals, EQ curves, andengineer tags). The engineer tags indicate, for specific tracks, whichone of a plurality of audio engineers generated the equalization profilefor the track. In some examples, the engineer tags may be represented bya one hot vector where each entry of the one hot vector corresponds toan engineer tag. In some examples, without informing the EQ neuralnetwork 402 of the engineer that generated the equalization profile fora track, the EQ neural network 402 may ultimately average the relativestylistic differences between different audio engineers. For example, ifa first set of reference audio signals has EQ curves generated by anaudio engineer that generally emphasizes the bass frequency range more,while a second set of reference audio signals has EQ curves generated byan audio engineer that generally emphasizes the middle frequency rangemore, the EQ neural network 402 may cancel these relative differenceduring training if it is unaware which audio engineer generated the EQcurves. By providing the engineer tags associated with the ones of theplurality of reference audio signals and corresponding EQ curves, the EQneural network 402 intelligently learns to recognize differentequalization styles and effectively utilize such styles when providingthe output 414 (e.g., EQ gains/cuts 241) in response to the EQ inputfeature set 239. In some examples, the EQ neural network 402 is trainedby associating samples of ones of the reference audio signals in thetraining data 408 with the known EQ curves for the reference audiosignals.

In some examples, reference audio signals can be generated by takingprofessionally engineered tracks and deteriorating the audio by applyingequalization curves to target a match with a spectral envelope of trackthat is not professionally engineered (e.g., from a lesser knownartist). The EQ neural network 402 can then be trained to revert thedeterioration by applying equalization curves to restore the track toits original quality level. Thus, any professionally engineered trackcan be utilized with this deterioration technique to enable high volumetraining.

In some examples, a loss function can be utilized for training the EQneural network 402. For example, Equation 3, represents one example lossfunction that can be utilized, where gi is the ground truth gain valuein bin “i,” and ĝ_(i) is the predicted value for that bin:

$\begin{matrix}{L = {\sum\limits_{i = 1}^{25}\; \left( {g_{i} - {\hat{g}}_{i}} \right)^{2}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Once a desired neural network behavior has been achieved (e.g., amachine has been trained to operate according to a specified threshold,etc.), the neural network can be deployed for use (e.g., testing themachine with “real” data, etc.). In some examples, the neural networkcan then be used without further modifications or updates to the neuralnetwork parameters (e.g., weights).

In some examples, during operation, neural network classifications canbe confirmed or denied (e.g., by an expert user, expert system,reference database, etc.) to continue to improve neural networkbehavior. The example neural network is then in a state of transferlearning, as parameters for classification that determine neural networkbehavior are updated based on ongoing interactions. In some examples,the neural network such as the EQ neural network 402 can provide directfeedback to another process, such as an audio EQ scoring engine 404,etc. In some examples, the EQ neural network 402 outputs data that isbuffered (e.g., via the cloud, etc.) and validated before it is providedto another process.

In some examples, the EQ neural network 402 can be seeded with someinitial correlations and can then learn from ongoing experience. In someexamples, throughout the operational life of the audio EQ engine 118,the EQ neural network 402 is continuously trained via feedback and theexample audio EQ engine validator 406 can be updated based on the EQneural network 402 and/or additional audio playback settings trainingdata 408 as desired. In some examples, the EQ neural network 402 canlearn and evolve based on role, location, situation, etc.

In some examples, a level of accuracy of the model generated by the EQneural network 402 can be determined by an example audio EQ enginevalidator 406. In such examples, at least one of the audio EQ scoringengine 404 and the audio EQ engine validator 406 receive a set of audioplayback settings training data (e.g., training data 408). The audio EQscoring engine 404 of the illustrated example of FIG. 4 can determine aneffectiveness of the output 414 (e.g., the EQ gains/cuts 241) output bythe EQ neural network 402 in response to the input 412 (e.g., the EQinput feature set 239). In some examples, the audio EQ scoring engine404 communicates with the audio EQ engine validator 406 during avalidation procedure to determine how closely an output of the EQ neuralnetwork 402 in response to an input feature set corresponds to a knownEQ curve for the input 412 (e.g., the EQ input feature set 239). Forexample, the EQ input feature set 239 can be an audio sample for whichan audio engineer has provided an EQ curve, and the audio EQ enginevalidator 406 can compare the output (e.g., the EQ gains/cuts 241)output by the EQ neural network 402 with the EQ curve (e.g., gains/cuts)provided by the audio engineer.

The EQ neural network 402 of the illustrated example of FIG. 4, afterbeing trained, responds to the input 412 (e.g., the EQ input feature set239) by providing the output 414 (e.g., the EQ gains/cuts 241) to themedia unit 106. For example, the EQ neural network 402 can determine aplurality of equalization adjustments (e.g., the EQ gains/cuts 241)based on an inference associated with at least the reference audiosignals, the EQ curves, and the engineer tags. In some examples, the EQgains/cuts 241 include a plurality of volume adjustment values (e.g.,gains/cuts) corresponding to a plurality of frequency ranges. In someexamples, the EQ gains/cuts 241 include a plurality of volume adjustmentvalues corresponding to a plurality of frequency ranges. For example,the EQ gains/cuts 241 output by the EQ neural network 402 can includetwenty-four gain or cut values corresponding to the twenty-four criticalbands of hearing.

In some examples, the EQ neural network 402 may learn equalizationsettings based upon a user's input(s). For example, if a usercontinually adjusts the equalization in a particular manner (e.g.,increasing volume at bass frequencies, decreasing volume at treblefrequencies, etc.) the EQ neural network 402 may learn these adjustmentsand output the EQ gains/cuts 241 accounting for the user preference.

In some examples, the comparison will yield a level of accuracy of themodel generated by the EQ neural network 402 (e.g., if 95 comparisonyield a match and 5 yield an error, the model is 95% accurate, etc.). Insome examples, once the EQ neural network 402 reaches a desired level ofaccuracy (e.g., the EQ neural network 402 is trained and ready fordeployment), the audio EQ engine validator 406 can output the model tothe data store 224 of FIG. 2 for use by the media unit 106 to determineaudio playback settings.

While an example manner of implementing the audio EQ engine 118 of FIG.1 is illustrated in FIG. 4, one or more of the elements, processesand/or devices illustrated in FIG. 4 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example EQ neural network 402, the example audio EQ scoringengine 404, the example audio EQ engine validator 406, and/or, moregenerally, the example audio EQ engine 118 of FIG. 4 may be implementedby hardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example EQneural network 402, the example audio EQ scoring engine 404, the exampleaudio EQ engine validator 406, and/or, more generally, the example audioEQ engine 118 of FIG. 4 could be implemented by one or more analog ordigital circuit(s), logic circuits, programmable processor(s),programmable controller(s), graphics processing unit(s) (GPU(s)),digital signal processor(s) (DSP(s)), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example EQ neuralnetwork 402, the example audio EQ scoring engine 404, the example audioEQ engine validator 406, and/or, more generally, the example audio EQengine 118 of FIG. 4 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample audio EQ engine 118 of FIG. 4 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 4, and/or may include more than one of any or all ofthe illustrated elements, processes and devices. As used herein, thephrase “in communication,” including variations thereof, encompassesdirect communication and/or indirect communication through one or moreintermediary components, and does not require direct physical (e.g.,wired) communication and/or constant communication, but ratheradditionally includes selective communication at periodic intervals,scheduled intervals, aperiodic intervals, and/or one-time events.

Flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the media unit 106 of FIGS. 1 and 2are shown in FIGS. 5, 6, 11, 12, 14, and 15. The machine readableinstructions may be an executable program or portion of an executableprogram for execution by a computer processor such as the processor 1812shown in the example processor platform 1800 discussed below inconnection with FIG. 18. The program may be embodied in software storedon a non-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 1812, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor1812 and/or embodied in firmware or dedicated hardware. Further,although the example program is described with reference to theflowchart illustrated in FIGS. 5, 6, 11, 12, 14, and 15, many othermethods of implementing the example media unit 106 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined. Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

Flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the audio EQ engine 118 of FIGS. 1and 2 are shown in FIGS. 7 and 16. The machine readable instructions maybe an executable program or portion of an executable program forexecution by a computer processor such as the processor 1912 shown inthe example processor platform 1900 discussed below in connection withFIG. 19. The program may be embodied in software stored on anon-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 1912, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor1912 and/or embodied in firmware or dedicated hardware. Further,although the example program is described with reference to theflowcharts illustrated in FIGS. 7 and 16, many other methods ofimplementing the example audio EQ engine 118 may alternatively be used.For example, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the content profile engine 116 ofFIGS. 1 and 3 is shown in FIG. 10. The machine readable instructions maybe an executable program or portion of an executable program forexecution by a computer processor such as the processor 2012 shown inthe example processor platform 2000 discussed below in connection withFIG. 20. The program may be embodied in software stored on anon-transitory computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associatedwith the processor 2012, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor2012 and/or embodied in firmware or dedicated hardware. Further,although the example program is described with reference to theflowchart illustrated in FIG. 10, many other methods of implementing theexample content profile engine 116 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.Additionally or alternatively, any or all of the blocks may beimplemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware.

As mentioned above, the example processes of FIGS. 5, 6, 7, 10, 11, 12,14, 15, and 16 may be implemented using executable instructions (e.g.,computer and/or machine readable instructions) stored on anon-transitory computer and/or machine readable medium such as a harddisk drive, a flash memory, a read-only memory, a compact disk, adigital versatile disk, a cache, a random-access memory and/or any otherstorage device or storage disk in which information is stored for anyduration (e.g., for extended time periods, permanently, for briefinstances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term non-transitory computer readablemedium is expressly defined to include any type of computer readablestorage device and/or storage disk and to exclude propagating signalsand to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

First Implementation: Filter-Based Equalization

FIG. 5 is a flowchart representative of example machine readableinstructions 500 that may be executed to implement the media unit 106 ofFIGS. 1 and 2 to dynamically adjust media playback settings based onreal-time analysis of media characteristics according to the firstimplementation. With reference to the preceding figures and associateddescriptions, the example machine readable instructions 500 begin withthe example media unit 106 accessing an audio signal (block 502). Insome examples the signal transformer 204 accesses the input media signal202.

At block 504, the example media unit 106 transforms the audio signal toa frequency representation. In some examples, the signal transformer 204transforms the input media signal 202 into a frequency and/orcharacteristic representation (e.g., a CQT representation, an FFTrepresentation, etc.).

At block 506, the example media unit 106 inputs the frequencyrepresentation into an EQ neural network. In some examples, the EQ modelquery generator 206 inputs the frequency representation of the inputmedia signal 202 to the EQ neural network 402. In some examples, the EQmodel query generator 206 inputs the input media signal 202 to a modeloutput by the EQ neural network 402.

At block 508, the example media unit 106 accesses a plurality of filtersettings including gain, frequency, and Q values. In some examples, theEQ filter settings analyzer 208 accesses the plurality of filtersettings including gain, frequency, and Q values. In some examples, theEQ filter settings analyzer 208 accesses a plurality of filter settings(e.g., sets of filter settings) including gain, frequency, and Q valuesas output by the EQ neural network 402. In some examples, the EQ filtersettings analyzer 208 accesses one or more high shelf filters, one ormore low shelf filters, and/or one or more peaking filters as output bythe EQ neural network 402.

At block 510, the example media unit 106 selects one or more filters toapply to the input media signal 202. In some examples, the EQ filterselector 218 selects one or more filters to apply to the input mediasignal 202. For example, to implement a five-band filter, the EQ filterselector 218 may select one low-shelf filter, one high-shelf filter, andthree peaking filters out of the sets of filters output by the EQ neuralnetwork 402.

At block 512, the example media unit 106 calculates filter coefficientsbased on settings of the selected filter(s). In some examples, the EQfilter settings analyzer 208 calculates filter coefficients based onfilter settings of the selected filter(s) to enable application of theone or more filter(s) to the input media signal 202.

At block 514, the example media unit personalizes equalization settings.In some examples, the EQ personalization manager 210 personalizedequalization settings (e.g., personalized EQ settings). Detailed examplemachine readable instructions to personalize equalization settings andillustrated and described in connection with FIG. 6.

At block 516, the example media unit 106 applies the selected filter(s)with smoothing to transition from prior filter settings (e.g., prioraudio playback settings). In some examples, the EQ adjustmentimplementor 220 applies the selected filter(s), and transitions to thenew playback settings based on a smoothing filter as indicated by thesmoothing filter configurator 222. In some examples, the EQ adjustmentimplementor 220 may implement the EQ filters (e.g., audio playbacksettings) without a smoothing filter.

At block 518, the example media unit 106 determines if the updateduration threshold is satisfied. In some examples, the update monitor226 determines if the update duration threshold is satisfied. Forexample, if the update duration threshold is set to one second, theupdate monitor 226 determines whether one second has passed sinceprevious audio playback settings have been determined and implemented.In response to the update duration threshold being satisfied, processingtransfers to block 502. Conversely, in response to the update durationthreshold not being satisfied, processing transfers to block 520.

At block 520, the example media unit 106 determines if dynamic audioplayback settings adjustment is enabled. In response to dynamic audioplayback settings adjustment being enabled, processing transfers toblock 518. Conversely, in response to dynamic audio playback settingsadjustment not being enabled, processing terminates.

FIG. 6 is a flowchart representative of example machine readableinstructions 514 and/or example machine readable instructions 1106 thatmay be executed to implement the media unit 106 of FIGS. 1 and 2 topersonalize equalization settings. With reference to the precedingfigures and associated descriptions, the example machine readableinstructions 514 and/or the example machine readable instructions 1106begin with the example media unit 106 accessing past personalizationsettings (block 602).

At block 604, the example media unit 106 generates a personalized EQcurve based on past personalization settings or initiates a newpersonalized EQ curve. In some examples, the historical EQ manager 214generates a personalized EQ curve based on past personalization settingsor initiates a new personalized EQ curve.

At block 606, the example media unit 106 determines whether historicalEQ is enabled. In some examples the historical EQ manager 214 determineswhether historical EQ is enabled (e.g., historical equalization isenabled). In response to historical EQ being enabled, processingtransfers to block 608. Conversely, in response to historical EQ notbeing enabled, processing transfer to block 610.

At block 608, the example media unit 106 adjusts the personalized EQcurve based on EQ curves from a historical period. In some examples, thehistorical EQ manager 214 adjusts the personalized EQ curve based on EQcurves from a historical period (e.g., the past hour, the past day,etc.).

At block 610, the media example unit 106 determines whether userpreference data is available (e.g., data indicative of preferences of auser). In some examples, the user input analyzer 216 determines whetheruser preference data is available. For example, the user input analyzer216 may determine user EQ preferences based on instances where the userpressed a “like” button while listening to music. In response to userpreference data being available (e.g., availability of user preferencedata), processing transfers to block 612. Conversely, in response touser preference data not being available, processing transfers to block616.

At block 612, the media example unit 106 determines EQ parameters basedon past user preference inputs (e.g., “likes,” ratings, etc.). In someexamples, the user input analyzer 216 determine EQ parameters based onpast user preference inputs.

At block 614, the example media unit 106 adjusts the personalized EQcurve based on past user preference inputs. In some examples the userinput analyzer 216 adjusts the personalized EQ curve based on EQ curvesfrom a historical period.

At block 616, the example media unit 106 determines whether locationdata is available. In some examples, the device parameter analyzer 212determines whether location data is available. In response to locationdata being available (e.g., availability of location data), processingtransfers to block 618. Conversely, in response to location data notbeing available, processing transfers to block 620.

At block 618, the example media unit 106 adjusts the personalized EQcurve based on the location of the device. In some examples, the deviceparameter analyzer 212 adjusts the personalized EQ curve based on thelocation of the device. For example, if the device is at a gym, adifferent personalized EQ curve may be generated than if the device isat a workplace.

At block 620, the example media unit 106 determines whether a useridentification is available. In some examples, the device parameteranalyzer 212 determines whether a user identification is available. Inresponse to user identification being available (e.g., availability ofuser identification), processing transfers to block 622. Conversely, inresponse to user identification not being available, processingtransfers to block 624.

At block 622, the example media unit 106 adjusts the personalized EQcurve based on a user identification. For example, the device parameteranalyzer 212 may determine that a first user, who has a historicalprofile (e.g., according to the historical EQ manager 214) of listeningto mostly rock music, is using the media unit 106. In such an example,the device parameter analyzer 212 can adjust the personalized EQ curveto be more suitable to rock music. Consequently, data stored in thehistorical EQ manager 214 may be filterable based on a specific user, alocation, an app providing the audio, etc.

At block 624, the example media unit 106 determines whether sourceinformation is available. In some examples, the device parameteranalyzer 212 determines whether source information is available. Inresponse to source information being available (e.g., availability ofsource information), processing transfers to block 626. Conversely, inresponse to source information not being available, processing transfersto block 628.

At block 626, the example media unit 106 adjusts the personalized EQcurve based on source information. In some examples, the deviceparameter analyzer 212 adjusts the personalized EQ curve based on sourceinformation. For example, the source information may indicate a specificapp (e.g., a music app, a fitness app, an audiobook app, etc.) of amobile device. Based on the source of the input media signal 202, thepersonalized EQ curve can be adjusted.

At block 628, the example media unit 106 adjusts the selected EQfilter(s) to be applied to the input media signal 202 by blending thedynamically generated filter outputs with the personalized EQ curve. Forexample, weights may be applied to each of the dynamically generatedcurves (e.g., based on outputs from the query submitted to the EQ neuralnetwork) and the personalized EQ curves, and an average curve can begenerated and applied to the input media signal 202. This average curvetherefore accounts for both variances between tracks as well as personalpreferences. After block 628, the machine readable instructions 514and/or the machine readable instructions 1106 return to the machinereadable instructions 500 at block 516 and to the machine readableinstructions 1100 at block 1108, respectively.

FIG. 7 is a flowchart representative of example machine readableinstructions 700 that may be executed to implement the audio EQ engine118 of FIG. 4 to train the EQ neural network 402 according to the firstimplementation. With reference to the preceding figures and associateddescriptions, the example machine readable instructions 700 begin withthe example audio EQ engine 118 accessing a library of reference audiosignals (block 702). In some examples, the EQ neural network 402accesses the library of reference audio signals. The library ofreference audio signals includes audio signals for which audio playbacksettings have been determined (e.g., by an expert).

At block 704, the example audio EQ engine 118 accesses EQ parametersassociated with the reference audio signals. In some examples, the EQneural network 402 accesses EQ parameters (e.g., audio playbacksettings) associated with the reference audio signals. For example, theEQ neural network 402 may accesses one or more filters, one or more gainvalues, frequencies values, Q values, etc.

At block 706, the example audio EQ engine 118 selects a reference audiosignal of the plurality of reference audio signals. In some examples,the EQ neural network 402 selects a reference audio signal of theplurality of reference audio signals.

At block 708, the example audio EQ engine 118 samples the referenceaudio signal. In some examples, the EQ neural network 402 samples thereference audio signal by creating a pre-determined number of samplesout of the audio signal (e.g., three-hundred, five-hundred, etc.).

At block 710, the example audio EQ engine 118 associates the samples ofthe reference audio signal with EQ parameters (e.g., audio playbacksettings) corresponding to the reference audio signal. In some examples,the EQ neural network 402 associates the samples of the reference audiosignal with EQ parameters corresponding to the reference audio signal.

At block 712 the example audio EQ engine 118 determines whether thereare additional reference audio signals to use for training. In someexamples, the EQ neural network 402 determines whether there areadditional reference audio signals to use for training. In response tothere being additional reference audio signals to use for training,processing transfers to block 706. Conversely, in response to there notbeing additional reference audio signals to use for training, processingterminates.

FIG. 8A is a first spectrogram 800 a of an audio signal which hasundergone dynamic audio playback setting adjustment based on real-timeanalysis of audio characteristics, but without a smoothing filter. Thefirst spectrogram 800 a depicts frequency values in Hertz on thehorizontal axis 802 (e.g., the x-axis) and time values in seconds of anaudio signal on the vertical axis 804 (e.g., the y-axis). The shading ofthe first spectrogram 800 a represents the amplitude of the audio signalat a specific frequency and time for the audio signal. The shading ofthe first spectrogram 800 a depicts sharp transitions between audiosignal amplitudes at numerous frequencies. For example, shading of thefirst spectrogram 800 a transitions sharply between lighter shading anddarker shading within individual frequency bands, at least partially dueto the transitions between audio playback settings implemented by thedynamic audio playback settings adjustment techniques disclosed herein,as implemented without a smoothing filter.

FIG. 8B is a first plot 800 b depicting average gain values forfrequency values for the first spectrogram 800 a of FIG. 8A. The firstplot 800 b includes frequency values in Hertz on the horizontal axis 806(e.g., the x-axis) and average gain values in decibels on the verticalaxis 808 (e.g., the y-axis). A comparison of the first plot 800 b,representing average gain values of the audio signal with audio playbacksettings adjusted without smoothing, and the second plot 900 b,representing average gain values of the audio signal with audio playbacksettings adjusted with smoothing, illustrates the advantages of applyingthe smoothing filter when transition between audio playback settings.

FIG. 9A is a second spectrogram 900 a of an audio signal which hasundergone dynamic audio playback setting adjustment based on real-timeanalysis of audio characteristics including a smoothing filter. Thesecond spectrogram 900 a includes frequency values in Hertz on thehorizontal axis 902 (e.g., the x-axis) and time values in seconds on thevertical axis 904 (e.g., the (e.g., the y-axis). The second spectrogram900 a corresponds to the same original input audio signal of the firstspectrogram 800 a FIG. 8A, but a smoothing filter has been utilized whenapplying the audio playback settings throughout the track. Relative tothe first spectrogram 800 a, the second spectrogram 900 a depicts smooth(e.g., gradual) transitions between audio signal amplitudes at numerousfrequencies. For example, shading of the first spectrogram 800 atransitions smoothly between lighter shading and darker shading withinindividual frequency bands, as opposed to the relatively sharptransitions exhibited in the first spectrogram 800 a of FIG. 8A.

FIG. 9B is a second plot 900 b depicting average gain values forfrequency values in the second spectrogram 900 a of FIG. 9A. The secondplot 900 b includes frequency values in Hertz on the horizontal axis 906(e.g., the x-axis) and average gain values in decibels on the verticalaxis 908 (e.g., the y-axis). Relative to the first plot 800 b of FIG.8B, the second plot 900 b depicts smoother transitions between averagegain values in numerous frequency bands. For example, numerous sharptransitions in the average gain value that are visible around 77 Hz inthe first plot 800 b are not present in the second plot 900 b, whichdepicts a gradual, smooth decrease in average gain value around 77 Hz.

Second Implementation: Profile-Based Equalization

FIG. 10 is a flowchart representative of example machine readableinstructions 1000 that may be executed to implement the content profileengine 116 of FIGS. 1 and 3 to deliver profile information (e.g., theone or more profiles 229) along with a stream of content (e.g., theinput media signal 202) to a playback device. As described herein, insome examples, the content profile engine 116 determines and/or generateone or more profiles 229 for the input media signal 202 to be deliveredto the media device 102, the media device 104, and/or the media unit106, among other things. With reference to the preceding figures andassociated descriptions, the example machine readable instructions 1000begin when the content profile engine 116 accesses a stream of contentto be delivered to a playback device (block 1002). For example, thecontent retriever 302 may access the input media signal 202 from thecontent provider 114 that is providing the input media signal 202 to theplayback device over the network 112. As another example, the contentretriever 302 may access the input media signal 202 (e.g., a stream ofcontent) from the content provider 114 that is locally stored by theplayback device. As described herein, the content retriever 302 mayaccess various types of content streams, such as audio content streams,video streams, and so on. For example, the content retriever 302 mayaccess a stream of songs or other music, a stream of spoken content, apodcast, and so on.

At block 1004, the content profile engine 116 identifies a portion ofthe input media signal 202 (e.g., a portion of content within the streamof content) to be delivered to the playback device. For example, thecontent identifier 306 may identify the portion of the input mediasignal 202 using a variety of processes, including a comparison of afingerprint for the content to a set of reference fingerprintsassociated with known content, such as reference fingerprints generatedby the reference fingerprint generator 227. Of course, the contentidentifier 306 may identify the piece of content using otherinformation, such as metadata (e.g., information identifying anassociated title, artist, genre, and so on) associated with the piece ofcontent, information associated with the content provider 114, and soon.

In some examples, the content identifier 306 may identify a certaincategory type or genre associated with the portion of the input mediasignal 202 (e.g., the piece of content). For example, instead ofidentifying the input media signal 202 as a specific piece of content(e.g., a specific song, YouTube™ videos/clips, TV programs, movies,podcast, and so on), the content identifier 306 may identify a genre orcategory applied to the portion of the input media signal 202 (e.g., thepiece of content) using the techniques described herein.

At block 1006, the content profile engine 116 determines a profile forthe identified piece of content. For example, the profiler 308 maydetermine one or more characteristics for an entire portion of the pieceof content and/or may determine one or more characteristics for multipleportions of the portion of the input media signal 202 (e.g., the pieceof content), such as frames or blocks of frames of the content. Forexample, the one or more profiles 229 may include a first set of one ormore characteristics for a first portion of the input media signal 202(e.g., the piece of content), a second set of one or morecharacteristics for a second portion of the input media signal 202(e.g., the piece of content), and so on.

In some examples, the profiler 308 renders, generates, creates, and/orotherwise determines the one or more profiles 229 for the input mediasignal 202 (e.g., piece of content), such as audio content having avariety of different characteristics. For example, the determined orgenerated the one or more profiles 229 may include characteristicsassociated with equalization (EQ) settings, spatialization settings,virtualization settings, video settings, and so on.

At block 1008, the content profile engine 116 delivers the one or moreprofiles 229 to the playback device. For example, the profiler 308 maydeliver the one or more profiles 229 to the playback device over thenetwork 112 or via other communication channels.

For example, the content profile engine 116 may access a piece ofcontent that is a song to be streamed to a playback device that is a carstereo, identify the song as a specific song, which is associated with agenre of “classical music,” determine a profile that includes a set ofequalization settings to be used when playing the song via the carstereo (e.g., signal strength indicators for different frequencieswithin the song, speaker spatialization settings, and so on), anddeliver the profile to the car stereo to be consumed by a networkassociated with the car stereo, such as a car area network (CAN), whichcontrols the operation of the car stereo.

In another example, the content profile engine 116 may access a piece ofcontent that is a movie to be streamed via a broadcast network or theInternet to a playback device that is a TV set or set top box, identifythe movie as being a specific movie as a specific movie, which isassociated with a genre of “action”, and as possessing a lot of fastaction sequences, determine a profile that includes a set of imageprocessing settings to be used when playing the movie via the TV set orother device (e.g. color palette settings, frame rate upscalingsettings, contrast enhancement settings for low contrast scenes, etc.),and deliver the profile to the TV set or other device for adjusting therendering and thus the content experience by the user.

FIG. 11 is a flowchart representative of example machine readableinstructions 1100 that may be executed to implement the media unit 106of FIGS. 1 and 2 to play content using modified playback settings. Asdescribed herein, in some examples, the media unit 106 modifies oradjusts the playback of content by the playback device (e.g., the mediadevice 102, the media device 104, and/or the media unit 106), amongother things. With reference to the preceding figures and associateddescriptions, the example machine readable instructions 1100 begin whenthe media unit 106 receives and/or accesses a stream of content at orassociated with a playback device (block 1102). For example, the mediaunit 106, and/or, more specifically, the synchronizer 228 may access theinput media signal 202 (e.g., a content stream) to be played by aplayback device.

At block 1104, the media unit 106 accesses profile informationassociated with the stream of content. For example, the media unit 106,and more specifically, the synchronizer 228 may receive a profile orprofile information that is generated by the content profile engine 116.As described herein, the content profile engine 116 may determine theprofile by identifying the stream of content based on a comparison offingerprints associated with the stream of content to a set offingerprints associated with known content, and select or otherwisedetermine the one or more profiles 229 that is associated with theidentified input media signal 202 (e.g., stream of content).

The one or more profiles 229 may include various types of information,such as information identifying a category or genre associated with thesong, information identifying a mood associated with the song, such asupbeat mood, a relaxed mood, a soft mood, and so on, informationidentifying signal strength parameters for different frequencies withinthe content, such as low frequencies for bass and other similar tones,high frequencies for spoken or sung tones, prosodic information and/orlanguage information obtained from spoken content, and so on.

Additionally or alternatively, the one or more profiles 229 may includeinformation identifying a category or genre associated with video, or asegment of a video clip, information identifying a mood associated withthe video, information identifying brightness, color palette, colorcontrast, luminance range, blurriness, display format, video sceneinformation, information obtained from visual object detection and/orrecognition, or face detection and or recognition, or broadcast logodetection and/or recognition algorithms, presence and/or content of textor subtitles, presence and/or content of watermarks, and so on.

At block 1106, the media unit 106 personalizes equalization settings. Insome examples, the EQ personalization manager 210 personalizesequalization settings. Detailed instructions to personalize equalizationsettings are illustrated and described in connection with FIG. 6.

At block 1108, the media unit 106 modifies the playback of the inputmedia signal 202 (e.g., stream of content) based on the accessed profileinformation and/or based on the personalized EQ profile generated atblock 1106. For example, the EQ adjustment implementor 220 may modifyplayback of the input media signal 202 on a playback device based on ablended equalization generated based on the one or more profiles 229 andthe personalized EQ profile. In another example, the EQ adjustmentimplementor 220 may apply information within the one or more profiles229 to modify or adjust the settings of an equalizer of the playbackdevice, in order to adjust and/or tune the equalization during theplayback of the input media signal 202 (e.g., stream of content). Inaddition to the equalization, the EQ adjustment implementor 220 mayadjust a variety of different playback settings, such as virtualizationsettings, spatialization settings, and so on.

In some examples, the media unit 106 may access a profile that includesmultiple settings that relate to different portions of the content. Forexample, a song may include portions having different tempos, and thecorresponding profile generated for the song may include first portionhaving a setting of “slow,” a second portion having a setting of “fast,”and a third portion having the setting of “slow,” among other things.The media unit 106, which may receive the profile from a differentplatform than the playback device, may synchronize the profile to thesong in order to accurately adjust the playback settings using themultiple settings contained by the profile.

FIG. 12 is a flowchart representative of example machine readableinstructions 1200 that may be executed to implement the media unit 106of FIGS. 1 and 2 to adjust playback settings based on profileinformation associated with content. For example, the media unit 106 canadjusting playback settings based on profile information associated withcontent, according to some examples. With reference to the precedingfigures and associated descriptions, the example machine readableinstructions 1200 begin when the media unit 106 accesses the one or moreprofiles 229 for the input media signal 202 (e.g., the piece of content)(block 1202). For example, the media unit 106, and/or, morespecifically, the synchronizer 228 may access different types ofprofiles, such as single setting profiles, multiple setting profiles,and so on.

At block 1204, the media unit 106 synchronizes the one or more profiles229 to the input media signal 202 (e.g., the piece of content). Forexample, the synchronizer 228 may utilize a fingerprint or fingerprintsassociated with the input media signal 202 (e.g., the piece of content)to synchronize the input media signal 202 (e.g., the piece of content)to the one or more profiles 229. The one or more profiles 229 mayinclude information that relates one or more settings to a knownfingerprint for the piece of content and aligns the settings to aportion of the input media signal 202 (e.g., the piece of content) inorder to synchronize the one or more profiles 229 to the piece ofcontent during playback of the input media signal 202. As anotherexample, the synchronizer 228 may identify various audio events withinthe piece of content (e.g., a snare hit, the beginning of a guitar solo,an initial vocal), and align the one or more profiles 229 to the eventswithin the input media signal 202, in order to synchronize the one ormore profiles 229 to the piece of content during playback of the inputmedia signal 202.

At block 1206, the media unit 106 modifies the playback of the inputmedia signal 202 utilizing the playback device (e.g., the media device102, the media device 104, the media unit 106, etc.) based on thesynchronized profile for the input media signal 202. For example, the EQadjustment implementor 220 may apply information within the one or moreprofiles 229 to modify or adjust the settings of an equalizer of theplayback device, in order to adjust and/or tune the equalization duringthe playback of the input media signal 202 (e.g., the stream ofcontent). Likewise, when the content is video, the one or more profiles229 may be used to adjust video-related settings.

FIGS. 13A-13B are block diagrams of example content profiles, inaccordance with the teachings of this disclosure. FIG. 13A depicts acontent profile 1300 a that includes a single setting 1302, or “mood #1”for an entire piece of content. On the other hand, FIG. 13B depicts acontent profile 1300 b that includes multiple different settings for thepiece of content. For example, the content profile 1300 b includes afirst setting 1304 (e.g., “mood #1”), a second setting 1306 (e.g., “mood#2”), a third setting 1308 (e.g., “mood #3”), and a fourth setting 1310(e.g., “mood #4), among other settings. Therefore, in some examples, themedia unit 106 may utilize complex or multilayered profiles, whichinclude different settings to be applied to different portions ofcontent, in order to dynamically adjust the playback experience of thecontent at different times during the playback of the content, amongother things.

Thus, the systems and methods described herein may provide a platformthat facilitates a real-time, or near real-time, processing and deliveryof profile information (e.g., a content profile) to a playback device,which utilizes the content profile to adjust a playback experience(e.g., video and/or audio experience) associated with playing thecontent to users, among other things. This may entail buffering thecontent before rendering of the content until a profile can be retrievedor predicted. In one example, a specific profile may be applied based onusage history (e.g. the user has consumed a specific content typeassociated with a specific profile at this time of day/week for the pastseveral days/weeks, so the same profile will be applied again afterdetermination of the usage pattern). In another example, the user hasearlier established preference of a specific profile with a specifictype of content (e.g. a video clip categorized as TV drama), so goingforward content that profile will be automatically applied for the sameor similar types of content. Another way of predicting a profile for auser may be through applying collaborative filtering methods, whereprofiles of other users are inferred on a particular user based on usagepatterns, demographic information, or any other information about a useror user group. Yet another example is including device settings such ascontent source setting, e.g. the selected input on a TV set, such as theinput that connects to a set top box, vs. the input that connects to aDVD player or game console, to determine or influence the profileselection.

Many playback devices may utilize such a platform, including: (1) A carstereo system that receives and plays content from an online, satellite,or terrestrial radio station and/or from a locally stored content player(e.g., CD player, MP3 player, and so on); (2) A home stereo system thatreceives and plays content from an online, satellite, or terrestrialradio station and/or from a locally stored content player (e.g., CDplayer, MP3 player, a TV set, a Set-Top-Box (STB), a game console, andso on); (3) A mobile device (e.g., smart phone or tablet) that receivesand plays content (e.g., video and/or audio) from an online, satellite,or terrestrial radio station and/or from a locally stored content player(e.g., MP3 player); and so on.

In some examples, the systems and methods may enhance and/or optimizelow quality or low volume recordings and other content. For example, thecontent profile engine 116 may identify a stream of content (e.g., ahomemade podcast) as having low audio quality, and generate a profilefor the low-quality stream of content that includes instructions toboost the playback of the content. The media unit 106 may then adjustthe playback settings of the playback device (e.g., a mobile device, themedia device 102, the media device 104, the media unit 106) to boost thefidelity of the playback of the low-quality content, among other things.

In some examples, the systems and methods may diminish the quality ofcertain types of content, such as advertisements within a contentstream. For example, the content profile engine 116 may identify astream of content includes a commercial break and generate a profile forthe stream of content that lowers the playback quality during thecommercial break. The media unit 106 may then adjust the playbacksettings of the playback device (e.g., a mobile device, the media device102, the media device 104, the media unit 106) to lower the fidelity ofthe playback of the content during the commercial break, among otherthings. Of course, other scenarios may be possible.

Third Implementation: Thresholding-Based Equalization

FIG. 14 is a flowchart representative of example machine readableinstructions 1400 that may be executed to implement the media unit 106of FIGS. 1 and 2 to perform audio equalization according to the thirdimplementation. With reference to the preceding figures and associateddescription, the example machine readable instructions 1400 begin withthe example media unit 106 storing the input media signal 202 in abuffer (block 1402). In some examples, the example buffer manager 230stores the input media signal 202 in the data store 224. In someexamples, the buffer manager 230 removes portions of the input mediasignal 202 which have exceeded their storage duration in the buffer(e.g., ten seconds, thirty seconds, etc.).

At block 1404, the example media unit 106 performs a frequency transformon the buffered audio. In some examples, the time to frequency domainconverter 232 performs a frequency transform (e.g., an FFT) on theportion of the input media signal 202 in the buffer.

At block 1406, the example media unit 106 computes average values andstandard deviation values for the linear-spaced frequency binsthroughout the duration of the buffer. In some examples, the volumecalculator 234 computes average values and standard deviation values forthe linear-spaced frequency bins throughout the duration of the buffer.In some examples, the average volume values may be computed in adifferent domain (e.g., a time domain) or with a different unit spacing(e.g., a logarithmic spacing).

At block 1408, the example media unit 106 computes a pre-equalizationRMS value based on a frequency representation of the input media signal202. In some examples, the energy calculator 236 computes apre-equalization RMS value based on the frequency representation of theinput media signal 202. In some examples, the energy calculator 236utilizes a different type of calculation to determine the energy valueof the input media signal 202.

At block 1410, the example media unit 106 inputs average values andstandard deviation values for linear-spaced bins, along with arepresentation of an engineer tag into the EQ neural network 402. Insome examples, the input feature set generator 238 inputs the averagevalues and standard deviation values for linear-spaced frequency binsthroughout the duration of the buffer to the EQ neural network 402. Fornon-reference or otherwise unidentified audio, the engineer tag is setto one particular value in a set of possible values. For example, theinput feature set generator 238 can be configured such that the engineertag is always set to a specific engineer indication when the audio isunidentified. In some examples, the engineer tag is represented as avector with one of the vector elements being set to “1” for the selectedengineer and the remaining vector elements being set to “0.” In someexamples, average and/or standard deviation values for the input mediasignal 202 may be input into the EQ neural network 402 in another form(e.g., a time domain format, an instantaneous volume instead of average,etc.).

At block 1412, the example media unit 106 receives gain/cut values forlog-spaced frequency bins from the EQ neural network 402. In someexamples, the volume adjuster 242 receives gain/cut values forlog-spaced frequency bins from the EQ neural network 402. In someexamples, the gains/cut values may be in a linear-spaced frequencyrepresentation and/or another domain.

At block 1414, the example media unit 106 converts the linear-spacedaverage frequency representation of the input media signal 202 to alog-spaced average frequency representation. In some examples, thevolume adjuster 242 converts the linear-spaced average frequencyrepresentation of the input media signal 202 to a log-spaced frequencyrepresentation in order to apply the EQ gains/cuts 241 which have beenreceived in a log-spaced format. In some examples, if the EQ gains/cuts241 are received in a different format, the volume adjuster 242 adjuststhe average frequency representation of the input media signal 202 tocorrespond to the same format as the EQ gains/cuts 241.

At block 1416, the example media unit 106 applies the gains/cuts to thelog-spaced average frequency representation to determine an equalizedlog-spaced frequency representation. In some examples, the volumeadjuster 242 applies the gains/cuts to the log-spaced average frequencyrepresentation to determine an equalized log-spaced average frequencyrepresentation of the input media signal 202. As in all steps of themachine readable instructions 1400, in some examples, applying thegains/cuts to the average representation of the incoming audio signalmay be done in a different domain and/or with a different unit spacing.

At block 1418, the example media unit 106 executes thresholding tosmooth the equalization curve. In some examples, the thresholdingcontroller 244 executes thresholding to smooth the equalization curve.Detailed instructions to execute thresholding to smooth the equalizationcurve are illustrated and described in connection with FIG. 15.

At block 1420, the example media unit 106 computes a post-equalizationRMS value. In some examples, the energy calculator 236 calculates apost-equalization RMS value based on equalized audio signal after thethresholding controller 244 has finished smoothing the equalizationcurve (e.g., after reduction of irregularities). In some examples, theenergy calculator 236 calculates another measure of energy of theequalized audio signal. In some examples, the energy calculator 236calculates the post-equalization RMS value after the EQ curve generator246 generates and applies the final equalization curve (e.g., in alinear-spaced frequency representation) to the input media signal 202.

At block 1422, the example media unit 106 determines a volumenormalization based on computation of pre-equalization RMS andpost-equalization RMS. In some examples, the energy calculator 236calculates a ratio (or other comparison metric) of the post-equalizationRMS and the pre-equalization RMS and the volume normalizer 248determines whether this ratio exceeds a threshold associated with amaximum acceptable change (e.g., associated with acceptable change) inenergy of the audio signal. In some such examples, in response to theratio exceeding the threshold, the volume normalizer 248 applies anormalization overall gain to the equalized audio signal. For example,if the overall energy of the audio signal after equalization is twicethe overall energy before equalization, the volume normalizer 248 canapply an overall gain of one half to normalize the overall volume of theaudio signal.

At block 1424, the example media unit 106 subtracts the averagefrequency representation from the equalized log-spaced frequencyrepresentation of the audio signal to determine a final equalizationcurve. In some examples, the EQ curve generator 246 subtracts theaverage frequency representation from the equalized log-spaced frequencyrepresentation of the audio signal to determine a final equalizationcurve.

At block 1426, the example media unit 106 applies the final equalizationcurve to the linear-spaced frequency representation of the input mediasignal 202. In some examples, the EQ curve generator 246 applies thefinal equalization curve, and additionally makes any overall gainadjustment indicated by the volume normalizer 248. In some examples, thevolume normalizer 248 may perform volume normalization before or afterthe EQ curve generator 246 applies the final equalization curve.

At block 1428, the example media unit 106 performs an inverse frequencytransform on the equalized frequency representation of the input mediasignal 202. In some examples, the frequency to time domain converter 250performs an inverse frequency transform on the equalized frequencyrepresentation of the input media signal 202 to generate the outputmedia signal 252.

At block 1430, the example media unit 106 determines whether to continueequalization. In response to continuing equalization, processingtransfers to block 1402. Conversely, in response to not continuingequalization, processing terminates.

FIG. 15 is a flowchart representative of example machine readableinstructions 1500 that may be executed to implement the media unit 106of FIGS. 1 and 2 to smooth an equalization curve according to the thirdimplementation. With reference to the preceding figures and associateddescription, the example machine readable instructions 1500 begin withthe example media unit 106 selecting a plurality of frequency values(block 1502). In some examples, the thresholding controller 244 selectsa plurality of frequency values to analyze for irregular changes involume (e.g., localized outliers). In some examples, the thresholdingcontroller 244 selects a set of adjacent frequency values (e.g., threediscrete consequent frequency values) to analyze at a time.

At block 1504, the example media unit 106 determines volumes at theplurality of frequency values. In some examples, the thresholdingcontroller 244 determines volumes at the plurality of frequency values.

At block 1506, the example media unit 106 determines the secondderivative of volume of the plurality of frequency values. In someexamples, the thresholding controller 244 determines the secondderivative of volume over the plurality of frequency values. In someexamples, the thresholding controller 244 utilizes another technique todetermine an amount of change in volume across the plurality offrequency values. One example technique to determine the secondderivative of volume over the plurality of frequency values includesutilizing Equation 1, described in connection with FIG. 2 above in thisdescription.

At block 1508, the example media unit 106 determines whether theabsolute value of the second derivative exceeds a threshold. In someexamples, the thresholding controller 244 determines whether theabsolute value of the second derivative exceeds the threshold. In someexamples, the thresholding controller 244 compares another calculationof the amount of change in volume across the plurality of frequencyvalues to the threshold. In response to the absolute value of the secondderivative exceeding the threshold, processing transfers to block 1510.Conversely, in response to the absolute value of the second derivativenot exceeding the threshold, processing transfers to block 1512.

At block 1510, the example media unit 106 adjusts volume levels of acentral one of the plurality of values to be a midpoint between volumelevels at adjacent frequency values. In some examples, the thresholdingcontroller 244 adjusts volume levels of a central one of the pluralityof values to be a midpoint between volume levels at adjacent frequencyvalues. In some examples, the thresholding controller 244 utilizesanother method to adjust the central one of the plurality of values tobe more similar to volumes at the adjacent frequency values, therebyreducing the irregularity in the equalization curve.

At block 1512, the example media unit 106 determines whether there areany additional frequency values to analyze. In some examples, thethresholding controller 244 determines whether there are any additionalfrequency values to analyze. In some examples, the thresholdingcontroller 244 iterates through analyzing all of the frequency valuesone or more times. In some examples, the thresholding controller 244iterates until all irregularities are removed, or until only a thresholdnumber of irregularities remain. In response to there being additionalfrequency values to analyze, processing transfers to block 1502.Conversely, in response to there not being additional frequency valuesto analyze, processing returns to the machine readable instructions ofFIG. 14 and proceeds to block 1420.

FIG. 16 is a flowchart representative of example machine readableinstructions 1600 that may be executed to implement the audio EQ engine118 of FIG. 4 to assemble a dataset to train and/or validate a neuralnetwork based on reference audio signals according to the thirdimplementation. With reference to the preceding figures and associateddescription, the example machine readable instructions 1600 begin withthe example audio EQ engine 118 accessing a library of reference audiosignals (block 1602). In some examples, the EQ neural network 402accesses a library of reference audio signals.

At block 1604, the example audio EQ engine 118 accesses equalizationcurves associated with the reference audio signals. In some examples,the EQ neural network 402 accesses the equalization curves associatedwith the reference audio signals.

At block 1606, the example audio EQ engine 118 accesses engineer tagsand/or other metadata associated with the reference audio signals. Insome examples, the EQ neural network 402 accesses engineer tags and/orother metadata associated with the reference audio signals.

At block 1608, the example audio EQ engine 118 associates samples of thereference audio signal with corresponding EQ curves and engineer tag(s).In some examples, the EQ neural network 402 associates samples of thereference audio signal with corresponding EQ curves and engineer tag(s).

At block 1610, the example audio EQ engine 118 determines whether thereare additional reference audio signals to use for training. In someexamples, the EQ neural network 402 determines whether there areadditional ones of the reference audio signals, EQ curves, engineer tagsto utilize for training. In response to there being additional referenceaudio signals to use for training, processing transfers to block 1602.Conversely, in response to there not being additional reference audiosignals to use for training, processing terminates.

FIG. 17A is an example first plot 1700 of an equalized audio signalprior to performing the smoothing techniques illustrated and describedin connection with FIG. 15.

The example first plot 1700 a includes an example frequency axis 1702,illustrating frequency values increasing from left-to-right (e.g.,across the x-axis). The first plot 1700 a includes an example volumeaxis 1704, illustrating volume values increasing from bottom-to-top(e.g., across the y-axis). In general, the first plot 1700 a illustratesthat the audio signal has higher volume levels at lower frequency valuesand the volume generally decreases as the frequency values increase.However, the first plot 1700 a includes an example irregularity 1706.

The first plot 1700 a includes an example first frequency value 1708, anexample second frequency value 1710, and an example third frequencyvalue 1712. The first frequency value 1708 corresponds to an examplefirst volume 1714, the second frequency value 1710 corresponds to anexample second volume 1716, and the third frequency value 1712corresponds to an example third volume 1718. If the media unit 106 wereto execute a thresholding procedure (e.g., via the thresholdingcontroller 244) on the signal illustrated in the first plot 1700 a, itmay detect the irregularity 1706 (e.g., a localized outlier), since thevolume changes significantly between the first frequency value 1708 andthe second frequency value 1710, as well as between the second frequencyvalue 1710 and the third frequency value 1712. If the thresholdingcontroller 244 calculate the second derivative of volume (or othermeasure of volume change) between the volume levels at the firstfrequency value 1708, the second frequency value 1710, and the thirdfrequency value 1712, it may determine that the second derivativeexceeds a threshold and corresponds to the irregularity 1706.

FIG. 17B is an example second plot 1700 b of the audio signal in FIG.17A after performing the smoothing techniques illustrated and describedin connection with FIG. 15. In the illustrated example of FIG. 17B,after detecting the irregularity 1706, the thresholding controller 244adjusts a volume level associated with the second frequency value 1710(e.g., a central one of the three frequency values under analysis). Thesecond plot 1700 b of FIG. 17B is substantially identical to the firstplot 1700 a, except that the second frequency value 1710 corresponds toan example fourth volume 1720 instead of the prior second volume 1716.In the illustrated example, the thresholding controller 244 adjusted thesecond volume 1716 to be the fourth volume 1720 by setting the volume atthe second frequency value 1710 to the midpoint of the first volume 1714and the third volume 1718. In the illustrated example, the remainder ofthe equalization curve between these frequency values is then generatedas a smooth line. In the illustrated example of FIG. 17B, the adjustedportion of the equalization curve is illustrated as a dashed lineconnecting the first volume 1714, the fourth volume 1720, and the thirdvolume 1718. The thresholding controller 244 may utilize any othertechnique to adjust volume levels at detected irregularities.

FIG. 18 is a block diagram of an example processor platform 1800structured to execute the instructions of FIGS. 5, 6, 11, 12, 14, and 15to implement the media unit 106 of FIGS. 1 and 2. The processor platform1800 can be, for example, a server, a personal computer, a workstation,a self-learning machine (e.g., a neural network), a mobile device (e.g.,a cell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 1800 of the illustrated example includes aprocessor 1812. The processor 1812 of the illustrated example ishardware. For example, the processor 1812 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor 1812 may be a semiconductor based (e.g., silicon based)device. In this example, the processor 1812 implements the examplesignal transformer 204, the example EQ model query generator 206, theexample EQ filter settings analyzer 208, the example EQ personalizationmanager 210, the example device parameter analyzer 212, the examplehistorical EQ manager 214, the example user input analyzer 216, theexample EQ filter selector 218, the example EQ adjustment implementor220, the example smoothing filter configurator 222, the example datastore 224, the example update monitor 226, the example fingerprintgenerator 227, the example synchronizer 228, the example buffer manager230, the example time to frequency domain converter 232, the examplevolume calculator 234, the example energy calculator 236, the exampleinput feature set generator 238, the example EQ manager 240, the examplevolume adjuster 242, the example thresholding controller 244, theexample EQ curve generator 246, the example volume normalizer 248,and/or the example frequency to time domain converter 250.

The processor 1812 of the illustrated example includes a local memory1813 (e.g., a cache). The processor 1812 of the illustrated example isin communication with a main memory including a volatile memory 1814 anda non-volatile memory 1816 via a bus 1818. The volatile memory 1814 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1816 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1814,1816 is controlled by a memory controller.

The processor platform 1800 of the illustrated example also includes aninterface circuit 1820. The interface circuit 1820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1822 are connectedto the interface circuit 1820. The input device(s) 1822 permit(s) a userto enter data and/or commands into the processor 1812. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1824 are also connected to the interfacecircuit 1820 of the illustrated example. The output devices 1824 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1820 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1826. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1800 of the illustrated example also includes oneor more mass storage devices 1828 for storing software and/or data.Examples of such mass storage devices 1828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine readable instructions 1832 of FIG. 18, the machine readableinstructions 500 of FIG. 5, the machine readable instructions 514 ofFIG. 6, the machine readable instructions 1100 of FIG. 11, the machinereadable instructions 1106 of FIG. 6, the machine readable instructions1200 of FIG. 12, the machine readable instructions 1400 of FIG. 14,and/or the machine readable instructions 1418 of FIG. 15 may be storedin the mass storage device 1828, in the volatile memory 1814, in thenon-volatile memory 1816, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

FIG. 19 is a block diagram of an example processor platform 1900structured to execute the instructions of FIGS. 7 and 16 to implementthe audio EQ engine 118 of FIGS. 1 and 4. The processor platform 1900can be, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 1900 of the illustrated example includes aprocessor 1912. The processor 1912 of the illustrated example ishardware. For example, the processor 1912 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor 1912 may be a semiconductor based (e.g., silicon based)device. In this example, the processor 1912 implements the example EQneural network 402, the example audio EQ scoring engine 404, and/or theexample audio EQ engine validator 406.

The processor 1912 of the illustrated example includes a local memory1913 (e.g., a cache). The processor 1912 of the illustrated example isin communication with a main memory including a volatile memory 1914 anda non-volatile memory 1916 via a bus 1918. The volatile memory 1914 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 1916 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 1914,1916 is controlled by a memory controller.

The processor platform 1900 of the illustrated example also includes aninterface circuit 1920. The interface circuit 1920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 1922 are connectedto the interface circuit 1920. The input device(s) 1922 permit(s) a userto enter data and/or commands into the processor 1912. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 1924 are also connected to the interfacecircuit 1920 of the illustrated example. The output devices 1924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 1920 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 1920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 1926. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 1900 of the illustrated example also includes oneor more mass storage devices 1928 for storing software and/or data.Examples of such mass storage devices 1928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine readable instructions 1932 of FIG. 19, the machine readableinstructions 700 of FIG. 7, and/or the machine readable instructions1600 of FIG. 16 may be stored in the mass storage device 1928, in thevolatile memory 1914, in the non-volatile memory 1916, and/or on aremovable non-transitory computer readable storage medium such as a CDor DVD.

FIG. 20 is a block diagram of an example processor platform 2000structured to execute the instructions of FIG. 10 to implement thecontent profile engine 116 of FIGS. 1 and 3. The processor platform 2000can be, for example, a server, a personal computer, a workstation, aself-learning machine (e.g., a neural network), a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), a personaldigital assistant (PDA), an Internet appliance, a DVD player, a CDplayer, a digital video recorder, a Blu-ray player, a gaming console, apersonal video recorder, a set top box, a headset or other wearabledevice, or any other type of computing device.

The processor platform 2000 of the illustrated example includes aprocessor 2012. The processor 2012 of the illustrated example ishardware. For example, the processor 2012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors, GPUs, DSPs,or controllers from any desired family or manufacturer. The hardwareprocessor 2012 may be a semiconductor based (e.g., silicon based)device. In this example, the processor 2012 implements the examplecontent retriever 302, the example fingerprint generator 304, theexample content identifier 306, the example profiler 308, and/or theexample profile data store 310.

The processor 2012 of the illustrated example includes a local memory2013 (e.g., a cache). The processor 2012 of the illustrated example isin communication with a main memory including a volatile memory 2014 anda non-volatile memory 2016 via a bus 2018. The volatile memory 2014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAMBUS® Dynamic Random AccessMemory (RDRAM®) and/or any other type of random access memory device.The non-volatile memory 2016 may be implemented by flash memory and/orany other desired type of memory device. Access to the main memory 2014,2016 is controlled by a memory controller.

The processor platform 2000 of the illustrated example also includes aninterface circuit 2020. The interface circuit 2020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 2022 are connectedto the interface circuit 2020. The input device(s) 2022 permit(s) a userto enter data and/or commands into the processor 2012. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 2024 are also connected to the interfacecircuit 2020 of the illustrated example. The output devices 2024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 2020 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 2020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 2026. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 2000 of the illustrated example also includes oneor more mass storage devices 2028 for storing software and/or data.Examples of such mass storage devices 2028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine readable instructions 2032 of FIG. 20 and/or the machinereadable instructions 1000 of FIG. 10 may be stored in the mass storagedevice 2028, in the volatile memory 2014, in the non-volatile memory2016, and/or on a removable non-transitory computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed thatdynamically adjust audio playback settings to adapt to changes inindividual tracks, changes between tracks, changes in genres, and/or anyother changes in audio signals by analyzing the audio signals andutilizing a neural network to determine optimal audio playback settings.Further, example methods, apparatus, and articles of manufacture havebeen disclosed that utilize a smoothing filter to intelligently adjustaudio playback settings without perceptible sharp shifts in volumelevels or equalization settings. Additionally, techniques disclosedherein enable an equalization approach which synthesizes dynamicadjustments from track-to-track as well as user preferences (representedin a personalized EQ profile).

Moreover, the example methods, apparatus, and articles of manufacturedisclosed herein intelligently equalize audio signals accounting fordifferences in source and/or other characteristics of the audio signals(e.g., genre, instruments present, etc.). Example techniques disclosedherein utilize a neural network trained with reference audio signalsthat have been equalized by audio engineers and input into the neuralnetwork with an indication of the specific audio engineers thatequalized the reference audio signals. Utilization of such trainingenables the neural network to provide expert equalization outputs andmake nuanced adjustments both between different tracks and even withinsame tracks. Further, example techniques disclosed herein improve uponthe equalization outputs of the neural network by performing athresholding technique to ensure that the final equalization curveapplied on the incoming audio signal is smooth and has minimalirregularities that may be perceptible to a listener.

Example methods, apparatus, systems, and articles of manufacture foraudio equalization are disclosed herein. Further examples andcombinations thereof include the following:

Example 1 includes an apparatus comprising an equalization (EQ) modelquery generator to generate a query to a neural network, the queryincluding a representation of a sample of an audio signal, an EQ filtersettings analyzer to access a plurality of audio playback settingsdetermined by the neural network based on the query, and determine afilter coefficient to apply to the audio signal based on the pluralityof audio playback settings, and an EQ adjustment implementor to applythe filter coefficient to the audio signal in a first duration.

Example 2 includes the apparatus of example 1, wherein therepresentation of the sample of the audio signal corresponds to afrequency representation of the sample of the audio signal.

Example 3 includes the apparatus of example 1, wherein the plurality ofaudio playback settings includes one or more filters, wherein each ofthe one or more filters include one or more respective gain values,respective frequency values, or respective quality factor valuesassociated with the sample of the audio signal.

Example 4 includes the apparatus of example 1, wherein the EQ filtersettings analyzer is to determine the filter coefficient to apply to theaudio signal based on a type of a filter associated with the filtercoefficient to be applied to the audio signal.

Example 5 includes the apparatus of example 1, wherein the EQ adjustmentimplementor is to apply a smoothing filter to the audio signal to reducesharp transitions in average gain values of the audio signal between thefirst duration and a second duration.

Example 6 includes the apparatus of example 1, further including asignal transformer to transform the audio signal to a frequencyrepresentation of the sample of the audio signal.

Example 7 includes the apparatus of example 1, wherein the EQ adjustmentimplementor is to adjust at least one of an amplitude characteristic, afrequency characteristic, or a phase characteristic of the audio signalbased on the filter coefficient.

Example 8 includes a non-transitory computer readable storage mediumcomprising instructions which, when executed, cause one or moreprocessors to at least generate a query to a neural network, the queryincluding a representation of a sample of an audio signal, access aplurality of audio playback settings determined by the neural networkbased on the query, determine a filter coefficient to apply to the audiosignal based on the plurality of audio playback settings, and apply thefilter coefficient to the audio signal in a first duration.

Example 9 includes the non-transitory computer readable storage mediumof example 8, wherein the representation of the sample of the audiosignal corresponds to a frequency representation of the sample of theaudio signal.

Example 10 includes the non-transitory computer readable storage mediumof example 8, wherein the plurality of audio playback settings includesone or more filters, wherein each of the one or more filters include oneor more respective gain values, respective frequency values, orrespective quality factor values associated with the sample of the audiosignal.

Example 11 includes the non-transitory computer readable storage mediumof example 8, wherein the instructions, when executed, cause the one ormore processors to determine the filter coefficient to apply to theaudio signal based on a type of a filter associated with the filtercoefficient to be applied to the audio signal.

Example 12 includes the non-transitory computer readable storage mediumof example 8, wherein the instructions, when executed, cause the one ormore processors to apply a smoothing filter to the audio signal toreduce sharp transitions in average gain values of the audio signalbetween the first duration and a second duration.

Example 13 includes the non-transitory computer readable storage mediumof example 8, wherein the instructions, when executed, cause the one ormore processors to transform the audio signal to a frequencyrepresentation of the sample of the audio signal.

Example 14 includes the non-transitory computer readable storage mediumof example 8, wherein the instructions, when executed, cause the one ormore processors to adjust at least one of an amplitude characteristic, afrequency characteristic, or a phase characteristic of the audio signalbased on the filter coefficient.

Example 15 includes a method comprising generating a query to a neuralnetwork, the query including a representation of a sample of an audiosignal, accessing a plurality of audio playback settings determined bythe neural network based on the query, determining a filter coefficientto apply to the audio signal based on the plurality of audio playbacksettings, and applying the filter coefficient to the audio signal in afirst duration.

Example 16 includes the method of example 15, wherein the representationof the sample of the audio signal corresponds to a frequencyrepresentation of the sample of the audio signal.

Example 17 includes the method of example 15, wherein the plurality ofaudio playback settings includes one or more filters, wherein each ofthe one or more filters include one or more respective gain values,respective frequency values, or respective quality factor valuesassociated with the sample of the audio signal.

Example 18 includes the method of example 15, further includingdetermining the filter coefficient to apply to the audio signal based ona type of a filter associated with the filter coefficient to be appliedto the audio signal.

Example 19 includes the method of example 15, further including applyinga smoothing filter to the audio signal to reduce sharp transitions inaverage gain values of the audio signal between the first duration and asecond duration.

Example 20 includes the method of example 15, further includingtransforming the audio signal to a frequency representation of thesample of the audio signal.

Example 21 includes an apparatus comprising an equalization (EQ) modelquery generator to generate a query to a neural network, the queryincluding a representation of a sample of an audio signal, an EQ filtersettings analyzer to access a plurality of audio playback settingsdetermined by the neural network based on the query, and determine afilter coefficient to apply to the audio signal based on the pluralityof audio playback settings, an EQ personalization manager to, generate apersonalized EQ setting, and an EQ adjustment implementor to blend thepersonalized EQ setting and the filter coefficient to generate a blendedequalization, and apply the blended equalization to the audio signal ina first duration.

Example 22 includes the apparatus of example 21, further including ahistorical EQ manager to generate the personalized EQ setting based onpast personalization settings, and in response to historicalequalization being enabled, adjust the personalized EQ setting based onEQ settings associated with a previous period of time.

Example 23 includes the apparatus of example 21, further including auser input analyzer to in response to availability of data indicative ofpreferences of a user, determine EQ parameters based on the dataindicative of preferences of the user, the EQ parameters correspondingto audio playback settings, and adjust the personalized EQ setting basedon the EQ parameters determined based on the data indicative ofpreferences of the user.

Example 24 includes the apparatus of example 21, further including adevice parameter analyzer to in response to availability of locationdata of a playback device, adjust the personalized EQ setting based onthe location data of the playback device, in response to availability ofidentification of a user, adjust the personalized EQ setting based on aprofile associated with the user, and in response to availability ofinformation associated with a source of the audio signal, adjust thepersonalized EQ setting based on the source of the audio signal.

Example 25 includes the apparatus of example 21, wherein the EQadjustment implementor is to apply weights to a first personalized EQsetting, a second personalized EQ setting, and the filter coefficient togenerate the blended equalization.

Example 26 includes the apparatus of example 21, wherein thepersonalized EQ setting is based on at least one of EQ settingsassociated with a previous period of time, data indicative ofpreferences of a user, location data of a playback device, a profileassociated with the user, or a source of the audio signal.

Example 27 includes the apparatus of example 21, wherein the EQadjustment implementor is to apply a smoothing filter to the audiosignal to reduce sharp transitions in average gain values of the audiosignal between the first duration and a second duration.

Example 28 includes a non-transitory computer readable storage mediumcomprising instructions which, when executed, cause one or moreprocessors to at least generate a query to a neural network, the queryincluding a representation of a sample of an audio signal, access aplurality of audio playback settings determined by the neural networkbased on the query, determine a filter coefficient to apply to the audiosignal based on the plurality of audio playback settings, generate apersonalized EQ setting, blend the personalized EQ setting and thefilter coefficient to generate a blended equalization, and apply theblended equalization to the audio signal in a first duration.

Example 29 includes the non-transitory computer readable storage mediumof example 28, wherein the instructions, when executed, cause the one ormore processors to generate the personalized EQ setting based on pastpersonalization settings, and in response to historical equalizationbeing enabled, adjust the personalized EQ setting based on EQ settingsassociated with a previous period of time.

Example 30 includes the non-transitory computer readable storage mediumof example 28, wherein the instructions, when executed, cause the one ormore processors to in response to availability of data indicative ofpreferences of a user, determine EQ parameters based on the dataindicative of preferences of the user, the EQ parameters correspondingto audio playback settings, and adjust the personalized EQ setting basedon the EQ parameters determined based on the data indicative ofpreferences of the user.

Example 31 includes the non-transitory computer readable storage mediumof example 28, wherein the instructions, when executed, cause the one ormore processors to in response to availability of location data of aplayback device, adjust the personalized EQ setting based on thelocation data of the playback device, in response to availability ofidentification of a user, adjust the personalized EQ setting based on aprofile associated with the user, and in response to availability ofinformation associated with a source of the audio signal, adjust thepersonalized EQ setting based on the source of the audio signal.

Example 32 includes the non-transitory computer readable storage mediumof example 28, wherein the instructions, when executed, cause the one ormore processors to apply weights to a first personalized EQ setting, asecond personalized EQ setting, and the filter coefficient to generatethe blended equalization.

Example 33 includes the non-transitory computer readable storage mediumof example 28, wherein the personalized EQ setting is based on at leastone of EQ settings associated with a previous period of time, dataindicative of preferences of a user, location data of a playback device,a profile associated with the user, or a source of the audio signal.

Example 34 includes the non-transitory computer readable storage mediumof example 28, wherein the instructions, when executed, cause the one ormore processors to apply a smoothing filter to the audio signal toreduce sharp transitions in average gain values of the audio signalbetween the first duration and a second duration.

Example 35 includes a method comprising generating a query to a neuralnetwork, the query including a representation of a sample of an audiosignal, accessing a plurality of audio playback settings determined bythe neural network based on the query, determining a filter coefficientto apply to the audio signal based on the plurality of audio playbacksettings, generating a personalized EQ setting, blending thepersonalized EQ setting and the filter coefficient to generate a blendedequalization, and applying the blended equalization to the audio signalin a first duration.

Example 36 includes the method of example 35, further includinggenerating the personalized EQ setting based on past personalizationsettings, and in response to historical equalization being enabled,adjusting the personalized EQ setting based on EQ settings associatedwith a previous period of time.

Example 37 includes the method of example 35, further including inresponse to availability of data indicative of preferences of a user,determining EQ parameters based on the data indicative of preferences ofthe user, the EQ parameters corresponding to audio playback settings,and adjusting the personalized EQ setting based on the EQ parametersdetermined based on the data indicative of preferences of the user.

Example 38 includes the method of example 35, further including inresponse to availability of location data of a playback device,adjusting the personalized EQ setting based on the location data of theplayback device, in response to availability of identification of auser, adjusting the personalized EQ setting based on a profileassociated with the user, and in response to availability of informationassociated with a source of the audio signal, adjusting the personalizedEQ setting based on the source of the audio signal.

Example 39 includes the method of example 35, further including applyingweights to a first personalized EQ setting, a second personalized EQsetting, and the filter coefficient to generate the blendedequalization.

Example 40 includes the method of example 35, wherein the personalizedEQ setting is based on at least one of EQ settings associated with aprevious period of time, data indicative of preferences of a user,location data of a playback device, a profile associated with the user,or a source of the audio signal.

Example 41 includes an apparatus comprising a synchronizer to, inresponse to receiving a media signal to be played on a playback device,access an equalization (EQ) profile corresponding to the media signal,an EQ personalization manager to generate a personalized EQ setting, andan EQ adjustment implementor to modify playback of the media signal onthe playback device based on a blended equalization generated based onthe EQ profile and the personalized EQ setting.

Example 42 includes the apparatus of example 41, further including ahistorical EQ manager to generate the personalized EQ setting based onpast personalization settings, and in response to historicalequalization being enabled, adjust the personalized EQ setting based onEQ settings associated with a previous period of time.

Example 43 includes the apparatus of example 41, further including auser input analyzer to in response to availability of data indicative ofpreferences of a user, determine EQ parameters based on the dataindicative of preferences of the user, the EQ parameters correspondingto audio playback settings, and adjust the personalized EQ setting basedon the EQ parameters determined based on the data indicative ofpreferences of the user.

Example 44 includes the apparatus of example 41, further including adevice parameter analyzer to in response to availability of locationdata of the playback device, adjust the personalized EQ setting based onthe location data of the playback device, in response to availability ofidentification of a user, adjust the personalized EQ setting based on auser profile, and in response to availability of information associatedwith a source of the media signal, adjust the personalized EQ settingbased on the source of the media signal.

Example 45 includes the apparatus of example 41, wherein the EQadjustment implementor is to apply weights to a first personalized EQsetting, a second personalized EQ setting, and the EQ profile togenerate the blended equalization.

Example 46 includes the apparatus of example 41, wherein thepersonalized EQ setting is based on at least one of EQ settingsassociated with a previous period of time, data indicative ofpreferences of a user, location data of the playback device, a userprofile, or a source of the media signal.

Example 47 includes the apparatus of example 41, wherein the EQ profileincludes playback attributes corresponding to at least one of (1)information identifying a category associated with a song, (2)information identifying a category associated with a video segment, (3)information identifying a mood associated with the song or the videosegment, or (4) information identifying signal strength parameters fordifferent frequencies with a portion of the media signal.

Example 48 includes a non-transitory computer readable storage mediumcomprising instructions which, when executed, cause one or moreprocessors to at least in response to receiving a media signal to beplayed on a playback device, access an equalization (EQ) profilecorresponding to the media signal, generate a personalized EQ setting,and modify playback of the media signal on the playback device based ona blended equalization generated based on the EQ profile and thepersonalized EQ setting.

Example 49 includes the non-transitory computer readable storage mediumof example 48, wherein the instructions, when executed, cause the one ormore processors to generate the personalized EQ setting based on pastpersonalization settings, and in response to historical equalizationbeing enabled, adjust the personalized EQ setting based on EQ settingsassociated with a previous period of time.

Example 50 includes the non-transitory computer readable storage mediumof example 48, wherein the instructions, when executed, cause the one ormore processors to in response to availability of data indicative ofpreferences of a user, determine EQ parameters based on the dataindicative of preferences of the user, the EQ parameters correspondingto audio playback settings, and adjust the personalized EQ setting basedon the EQ parameters determined based on the data indicative ofpreferences of the user.

Example 51 includes the non-transitory computer readable storage mediumof example 48, wherein the instructions, when executed, cause the one ormore processors to in response to availability of location data of theplayback device, adjust the personalized EQ setting based on thelocation data of the playback device, in response to availability ofidentification of a user, adjust the personalized EQ setting based on auser profile, and in response to availability of information associatedwith a source of the media signal, adjust the personalized EQ settingbased on the source of the media signal.

Example 52 includes the non-transitory computer readable storage mediumof example 48, wherein the instructions, when executed, cause the one ormore processors to apply weights to a first personalized EQ setting, asecond personalized EQ setting, and the EQ profile to generate theblended equalization.

Example 53 includes the non-transitory computer readable storage mediumof example 48, wherein the personalized EQ setting is based on at leastone of EQ settings associated with a previous period of time, dataindicative of preferences of a user, location data of the playbackdevice, a user profile, or a source of the media signal.

Example 54 includes the non-transitory computer readable storage mediumof example 48, wherein the EQ profile includes playback attributescorresponding to at least one of (1) information identifying a categoryassociated with a song, (2) information identifying a categoryassociated with a video segment, (3) information identifying a moodassociated with the song or the video segment, or (4) informationidentifying signal strength parameters for different frequencies with aportion of the media signal.

Example 55 includes a method comprising in response to receiving a mediasignal to be played on a playback device, accessing an equalization (EQ)profile corresponding to the media signal, generating a personalized EQsetting, and modifying playback of the media signal on the playbackdevice based on a blended equalization generated based on the EQ profileand the personalized EQ setting.

Example 56 includes the method of example 55, further includinggenerating the personalized EQ setting based on past personalizationsettings, and in response to historical equalization being enabled,adjusting the personalized EQ setting based on EQ settings associatedwith a previous period of time.

Example 57 includes the method of example 55, further including inresponse to availability of data indicative of preferences of a user,determining EQ parameters based on the data indicative of preferences ofthe user, the EQ parameters corresponding to audio playback settings,and adjusting the personalized EQ setting based on the EQ parametersdetermined based on the data indicative of preferences of the user.

Example 58 includes the method of example 55, further including inresponse to availability of location data of the playback device,adjusting the personalized EQ setting based on the location data of theplayback device, in response to availability of identification of auser, adjusting the personalized EQ setting based on a user profile, andin response to availability of information associated with a source ofthe media signal, adjusting the personalized EQ setting based on thesource of the media signal.

Example 59 includes the method of example 55, further including applyingweights to a first personalized EQ setting, a second personalized EQsetting, and the EQ profile to generate the blended equalization.

Example 60 includes the method of example 55, wherein the personalizedEQ setting is based on at least one of EQ settings associated with aprevious period of time, data indicative of preferences of a user,location data of the playback device, a user profile, or a source of themedia signal.

Example 61 includes an apparatus comprising a volume adjuster to apply aplurality of equalization adjustments to an audio signal to generate anequalized audio signal, the plurality of equalization adjustments outputfrom a neural network in response to an input feature set including anaverage volume representation of the audio signal, a thresholdingcontroller to detect an irregularity in a frequency representation ofthe audio signal after application of the plurality of equalizationadjustments, the irregularity corresponding to a change in volumebetween adjacent frequency values exceeding a threshold, and adjust avolume at a first frequency value of the adjacent frequency values toreduce the irregularity, an equalization (EQ) curve generator togenerate an EQ curve to apply to the audio signal when the irregularityhas been reduced, and a frequency to time domain converter to output theequalized audio signal in a time domain based on the EQ curve.

Example 62 includes the apparatus of example 61, further including anenergy calculator to determine a first root mean square (RMS) value ofthe frequency representation of the audio signal prior to application ofthe plurality of equalization adjustments, determine a second RMS valueof the frequency representation of the audio signal after reduction ofthe irregularity, and determine a ratio between the second RMS value andthe first RMS value.

Example 63 includes the apparatus of example 61, further including avolume normalizer to determine whether a ratio between (1) a first RMSvalue of the frequency representation of the audio signal afterreduction of the irregularity and (2) a second RMS value of thefrequency representation of the audio signal prior to application of theplurality of equalization adjustments exceeds a threshold associatedwith acceptable change in energy of the audio signal, and in response tothe ratio exceeding the threshold, apply a normalization of a gain ofthe frequency representation of the audio signal.

Example 64 includes the apparatus of example 61, wherein the pluralityof equalization adjustments includes a plurality of volume adjustmentvalues corresponding to a plurality of frequency ranges.

Example 65 includes the apparatus of example 61, wherein thethresholding controller is to select a plurality of frequency values inthe frequency representation of the audio signal, determine a pluralityof volume values associated with the plurality of frequency values,determine a second derivative of the volume over the plurality offrequency values, and in response to an absolute value of the secondderivative exceeding the threshold, adjust the volume at the firstfrequency value of the adjacent frequency values to reduce theirregularity.

Example 66 includes the apparatus of example 61, wherein the pluralityof equalization adjustments is based on at least reference audiosignals, EQ curves, and tags associated with a plurality of audioengineers that generated the EQ curves, and wherein the neural networkdetermines the plurality of equalization adjustments based on aninference associated with at least the reference audio signals, the EQcurves, and the tags associated with the plurality of audio engineers.

Example 67 includes the apparatus of example 66, wherein the inputfeature set includes the average volume representation of the audiosignal and average standard deviation measurements for frequency bins ofa frequency representation of the audio signal.

Example 68 includes a non-transitory compute readable storage mediumcomprising instructions which, when executed, cause one or moreprocessors to at least apply a plurality of equalization adjustments toan audio signal to generate an equalized audio signal, the plurality ofequalization adjustments output from a neural network in response to aninput feature set including an average volume representation of theaudio signal, detect an irregularity in a frequency representation ofthe audio signal after application of the plurality of equalizationadjustments, the irregularity corresponding to a change in volumebetween adjacent frequency values exceeding a threshold, adjust a volumeat a first frequency value of the adjacent frequency values to reducethe irregularity, generate an equalization (EQ) curve to apply to theaudio signal when the irregularity has been reduced, and output theequalized audio signal in a time domain based on the EQ curve.

Example 69 includes the non-transitory computer readable storage mediumof example 68, wherein the instructions, when executed, cause the one ormore processors to determine a first root mean square (RMS) value of thefrequency representation of the audio signal prior to application of theplurality of equalization adjustments, determine a second RMS value ofthe frequency representation of the audio signal after reduction of theirregularity, and determine a ratio between the second RMS value and thefirst RMS value.

Example 70 includes the non-transitory computer readable storage mediumof example 68, wherein the instructions, when executed, cause the one ormore processors to determine whether a ratio between (1) a first RMSvalue of the frequency representation of the audio signal afterreduction of the irregularity and (2) a second RMS value of thefrequency representation of the audio signal prior to application of theplurality of equalization adjustments exceeds a threshold associatedwith acceptable change in energy of the audio signal, and in response tothe ratio exceeding the threshold, apply a normalization of a gain ofthe frequency representation of the audio signal.

Example 71 includes the non-transitory computer readable storage mediumof example 68, wherein the plurality of equalization adjustment includesa plurality of volume adjustment values corresponding to a plurality offrequency ranges.

Example 72 includes the non-transitory computer readable storage mediumof example 68, wherein the instructions, when executed, cause the one ormore processors to select a plurality of frequency values in thefrequency representation of the audio signal, determine a plurality ofvolume values associated with the plurality of frequency values,determine a second derivative of the volume over the plurality offrequency values, and in response to an absolute value of the secondderivative exceeding the threshold, adjust the volume at the firstfrequency value of the adjacent frequency values to reduce theirregularity.

Example 73 includes the non-transitory computer readable storage mediumof example 68, wherein the plurality of equalization adjustments isbased on at least reference audio signals, EQ curves, and tagsassociated with a plurality of audio engineers that generated the EQcurves, and wherein the neural network determines the plurality ofequalization adjustments based on an inference associated with at leastthe reference audio signals, the EQ curves, and the tags associated withthe plurality of audio engineers.

Example 74 includes the non-transitory computer readable storage mediumof example 73, wherein the input feature set includes the average volumerepresentation of the audio signal and average standard deviationmeasurements for frequency bins of a frequency representation of theaudio signal.

Example 75 includes a method comprising applying a plurality ofequalization adjustments to an audio signal to generate an equalizedaudio signal, the plurality of equalization adjustments output from aneural network in response to an input feature set including an averagevolume representation of the audio signal, detecting an irregularity ina frequency representation of the audio signal after application of theplurality of equalization adjustments, the irregularity corresponding toa change in volume between adjacent frequency values exceeding athreshold, adjusting a volume at a first frequency value of the adjacentfrequency values to reduce the irregularity, generating an equalization(EQ) curve to apply to the audio signal when the irregularity has beenreduced, and outputting the equalized audio signal in a time domainbased on the EQ curve.

Example 76 includes the method of example 75, further includingdetermining a first root mean square (RMS) value of the frequencyrepresentation of the audio signal prior to application of the pluralityof equalization adjustments, determining a second RMS value of thefrequency representation of the audio signal after reduction of theirregularity, and determining a ratio between the second RMS value andthe first RMS value.

Example 77 includes the method of example 75, further includingdetermining whether a ratio between (1) a first RMS value of thefrequency representation of the audio signal after reduction of theirregularity and (2) a second RMS value of the frequency representationof the audio signal prior to application of the plurality ofequalization adjustments exceeds a threshold associated with acceptablechange in energy of the audio signal, and in response to the ratioexceeding the threshold, applying a normalization of a gain of thefrequency representation of the audio signal.

Example 78 includes the method of example 75, wherein the plurality ofequalization adjustments includes a plurality of volume adjustmentvalues corresponding to a plurality of frequency ranges.

Example 79 includes the method of example 75, further includingselecting a plurality of frequency values in the frequencyrepresentation of the audio signal, determining a plurality of volumevalues associated with the plurality of frequency values, determining asecond derivative of the volume over the plurality of frequency values,and in response to an absolute value of the second derivative exceedingthe threshold, adjusting the volume at the first frequency value of theadjacent frequency values to reduce the irregularity.

Example 80 includes the method of example 75, wherein the plurality ofequalization adjustments is based on at least reference audio signals,EQ curves, and tags associated with a plurality of audio engineers thatgenerated the EQ curves, and wherein the neural network determines theplurality of equalization adjustments based on an inference associatedwith at least the reference audio signals, the EQ curves, and the tagsassociated with the plurality of audio engineers.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: a volume adjuster toapply a plurality of equalization adjustments to an audio signal togenerate an equalized audio signal, the plurality of equalizationadjustments output from a neural network in response to an input featureset including an average volume representation of the audio signal; athresholding controller to: detect an irregularity in a frequencyrepresentation of the audio signal after application of the plurality ofequalization adjustments, the irregularity corresponding to a change involume between adjacent frequency values exceeding a threshold; andadjust a volume at a first frequency value of the adjacent frequencyvalues to reduce the irregularity; an equalization (EQ) curve generatorto generate an EQ curve to apply to the audio signal when theirregularity has been reduced; and a frequency to time domain converterto output the equalized audio signal in a time domain based on the EQcurve.
 2. The apparatus of claim 1, further including an energycalculator to: determine a first root mean square (RMS) value of thefrequency representation of the audio signal prior to application of theplurality of equalization adjustments; determine a second RMS value ofthe frequency representation of the audio signal after reduction of theirregularity; and determine a ratio between the second RMS value and thefirst RMS value.
 3. The apparatus of claim 1, further including a volumenormalizer to: determine whether a ratio between (1) a first RMS valueof the frequency representation of the audio signal after reduction ofthe irregularity and (2) a second RMS value of the frequencyrepresentation of the audio signal prior to application of the pluralityof equalization adjustments exceeds a threshold associated withacceptable change in energy of the audio signal; and in response to theratio exceeding the threshold, apply a normalization of a gain of thefrequency representation of the audio signal.
 4. The apparatus of claim1, wherein the plurality of equalization adjustments includes aplurality of volume adjustment values corresponding to a plurality offrequency ranges.
 5. The apparatus of claim 1, wherein the thresholdingcontroller is to: select a plurality of frequency values in thefrequency representation of the audio signal; determine a plurality ofvolume values associated with the plurality of frequency values;determine a second derivative of the volume over the plurality offrequency values; and in response to an absolute value of the secondderivative exceeding the threshold, adjust the volume at the firstfrequency value of the adjacent frequency values to reduce theirregularity.
 6. The apparatus of claim 1, wherein the plurality ofequalization adjustments is based on at least reference audio signals,EQ curves, and tags associated with a plurality of audio engineers thatgenerated the EQ curves, and wherein the neural network determines theplurality of equalization adjustments based on an inference associatedwith at least the reference audio signals, the EQ curves, and the tagsassociated with the plurality of audio engineers.
 7. The apparatus ofclaim 6, wherein the input feature set includes the average volumerepresentation of the audio signal and average standard deviationmeasurements for frequency bins of a frequency representation of theaudio signal.
 8. A non-transitory compute readable storage mediumcomprising instructions which, when executed, cause one or moreprocessors to at least: apply a plurality of equalization adjustments toan audio signal to generate an equalized audio signal, the plurality ofequalization adjustments output from a neural network in response to aninput feature set including an average volume representation of theaudio signal; detect an irregularity in a frequency representation ofthe audio signal after application of the plurality of equalizationadjustments, the irregularity corresponding to a change in volumebetween adjacent frequency values exceeding a threshold; adjust a volumeat a first frequency value of the adjacent frequency values to reducethe irregularity; generate an equalization (EQ) curve to apply to theaudio signal when the irregularity has been reduced; and output theequalized audio signal in a time domain based on the EQ curve.
 9. Thenon-transitory computer readable storage medium of claim 8, wherein theinstructions, when executed, cause the one or more processors to:determine a first root mean square (RMS) value of the frequencyrepresentation of the audio signal prior to application of the pluralityof equalization adjustments; determine a second RMS value of thefrequency representation of the audio signal after reduction of theirregularity; and determine a ratio between the second RMS value and thefirst RMS value.
 10. The non-transitory computer readable storage mediumof claim 8, wherein the instructions, when executed, cause the one ormore processors to: determine whether a ratio between (1) a first RMSvalue of the frequency representation of the audio signal afterreduction of the irregularity and (2) a second RMS value of thefrequency representation of the audio signal prior to application of theplurality of equalization adjustments exceeds a threshold associatedwith acceptable change in energy of the audio signal; and in response tothe ratio exceeding the threshold, apply a normalization of a gain ofthe frequency representation of the audio signal.
 11. The non-transitorycomputer readable storage medium of claim 8, wherein the plurality ofequalization adjustment includes a plurality of volume adjustment valuescorresponding to a plurality of frequency ranges.
 12. The non-transitorycomputer readable storage medium of claim 8, wherein the instructions,when executed, cause the one or more processors to: select a pluralityof frequency values in the frequency representation of the audio signal;determine a plurality of volume values associated with the plurality offrequency values; determine a second derivative of the volume over theplurality of frequency values; and in response to an absolute value ofthe second derivative exceeding the threshold, adjust the volume at thefirst frequency value of the adjacent frequency values to reduce theirregularity.
 13. The non-transitory computer readable storage medium ofclaim 8, wherein the plurality of equalization adjustments is based onat least reference audio signals, EQ curves, and tags associated with aplurality of audio engineers that generated the EQ curves, and whereinthe neural network determines the plurality of equalization adjustmentsbased on an inference associated with at least the reference audiosignals, the EQ curves, and the tags associated with the plurality ofaudio engineers.
 14. The non-transitory computer readable storage mediumof claim 13, wherein the input feature set includes the average volumerepresentation of the audio signal and average standard deviationmeasurements for frequency bins of a frequency representation of theaudio signal.
 15. A method comprising: applying a plurality ofequalization adjustments to an audio signal to generate an equalizedaudio signal, the plurality of equalization adjustments output from aneural network in response to an input feature set including an averagevolume representation of the audio signal; detecting an irregularity ina frequency representation of the audio signal after application of theplurality of equalization adjustments, the irregularity corresponding toa change in volume between adjacent frequency values exceeding athreshold; adjusting a volume at a first frequency value of the adjacentfrequency values to reduce the irregularity; generating an equalization(EQ) curve to apply to the audio signal when the irregularity has beenreduced; and outputting the equalized audio signal in a time domainbased on the EQ curve.
 16. The method of claim 15, further including:determining a first root mean square (RMS) value of the frequencyrepresentation of the audio signal prior to application of the pluralityof equalization adjustments; determining a second RMS value of thefrequency representation of the audio signal after reduction of theirregularity; and determining a ratio between the second RMS value andthe first RMS value.
 17. The method of claim 15, further including:determining whether a ratio between (1) a first RMS value of thefrequency representation of the audio signal after reduction of theirregularity and (2) a second RMS value of the frequency representationof the audio signal prior to application of the plurality ofequalization adjustments exceeds a threshold associated with acceptablechange in energy of the audio signal; and in response to the ratioexceeding the threshold, applying a normalization of a gain of thefrequency representation of the audio signal.
 18. The method of claim15, wherein the plurality of equalization adjustments includes aplurality of volume adjustment values corresponding to a plurality offrequency ranges.
 19. The method of claim 15, further including:selecting a plurality of frequency values in the frequencyrepresentation of the audio signal; determining a plurality of volumevalues associated with the plurality of frequency values; determining asecond derivative of the volume over the plurality of frequency values;and in response to an absolute value of the second derivative exceedingthe threshold, adjusting the volume at the first frequency value of theadjacent frequency values to reduce the irregularity.
 20. The method ofclaim 15, wherein the plurality of equalization adjustments is based onat least reference audio signals, EQ curves, and tags associated with aplurality of audio engineers that generated the EQ curves, and whereinthe neural network determines the plurality of equalization adjustmentsbased on an inference associated with at least the reference audiosignals, the EQ curves, and the tags associated with the plurality ofaudio engineers.